EARLY ALZHEIMER’S DISEASE DETECTION

SYSTEM USING DECISION TREE ALGORTIHM

NUR FATIN SHAMIMI BINTI MAMAT

BACHELOR OF COMPUTER SCIENCE

(SOFTWARE DEVELOPMENT)

UNIVERSITI SULTAN ZAINAL ABIDIN

2018

2

EARLY ALZHEIMER’S DISEASE DETECTION SYSTEM

USING DECISION TREE ALGORTIHM

NUR FATIN SHAMIMI BINTI MAMAT

Bachelor of Computer Science (Software Development)

Faculty of Informatics and Computing

Universiti Sultan Zainal Abidin, Terengganu, Malaysia

MEI 2018

3

DECLARATION

I hereby declare that this report is based on my original work except for quotations

and citations, which have been duly acknowledged. I also declare that it has not been

previously or concurrently submitted for any other degree at Universiti Sultan Zainal

Abidin or other institutions.

________________________________

Name : Nur Fatin Shamimi Binti Mamat

Date : ..................................................

4

CONFIRMATION

This is to confirm that: The research conducted and the writing of this report was under my

supervision.

Name : Pn.Nor Surayati Binti Mohamad Usop

Date : ..................................................

5

DEDICATION

In the name of Allah, the most Gracious and the Most Merciful, Alhamdulillah, All praise to

Allah who have guided and give me strength to finish and submit the development system

report in due time and without whose help this study which required untiring effort would

have not been possible to complete with time limits. On this special opportunity given to me,

I would like to express my sincere gratitude to my supervisor, MADAM NOR SURAYATI

BINTI MOHAMAD USOP for her supervision and inspiration throughout my final year

project and because of her careful review, criticism, encouragement and discussion have

greatly distribute in completing this proposed proposal for final project. Sincere thanks to all

my panel DR.SYARILLA IRYANI BINTI AHMAD SAANY, DR.ZAHRATUL AMANI

BINTI ZAKARIA and MADAM FAUZIAH BINTI AB.WAHAB because willing to give an

advise and motivate me in order to finish this final project. Also sincere thanks to my beloved

mentor MADAM MAIZAN BINTI MAT AMIN a caring and loving mentor for my final year

studying at UniSZA. Not forgotten to all my classmates and friends for their kindness and

moral support during my study. Last but not least a very personal words of thanks and special

appreciation to my parents, MAMAT BIN EMBONG and ZAITON BINTI YATIM, also to

my siblings for their understanding , endless love and encouragement.

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ABSTRACT

Alzheimer, an old age disease of people over 65 years causes problems with memory,

thinking and behaviour. This disease progresses very slow and its identification in

early stages is very difficult. The symptoms appear slowly and these gradually will

have worse effects. In its early stages, not only the patients themselves but their loved

ones are generally unable to accept that the patient is suffering from disease. On

average patients live of eight years after identification of Alzheimer, but patients

survive from 4 to 20 years depending on their age and other health conditions. Early

detection of Alzheimer’s and its stages is very important, because as it worsens it has

no cure and patients have very dreadful life before their death. Peoples lacking

knowledge and attention to current issues regarding Alzheimer’s disease make it

difficult to detect their loved one illness earlier. Detecting Alzheimer’s as early as

possible is important as studies show that interventions are performed on Alzheimer’s

patient after the initial examination results in cure or at least slow it down. During the

construction of this system I used Iterative Model to collect information by

reconstructing the steps in the Iterative Model and to locate the disease the system

uses the Decision tree to detect. In addition, this system provides the percentage of

detection of an Alzheimer’s patient, if a person is positively taken to Alzheimer’s

specialist for treatment, and if there is no need to see an Alzheimer’s specialist. The

Decision tree classifies the given data item using the values of its attributes. The

decision tree is initially constructed from a set of pre-classified data. With Early

Alzheimer’s Detection System, it can help family detect their loved one earlier.

7

ABSTRAK

Alzheimer, penyakit usia tua yang berusia lebih 65 tahun menyebabkan masalah

dengan ingatan, pemikiran dan tingkah laku. Penyakit ini sangat perlahan dan

pengenalannya pada peringkat awal sangat sukar. Gejala-gejala muncul perlahan-

lahan dan secara beransur-ansur akan membawa kepada kesan yang lebih teruk. Pada

peringkat awal, bukan sahaja pesakit itu sendiri tetapi orang yang mereka sayangi

juga tidak dapat menerima bahawa pesakit itu menderita penyakit Alzheimer. Rata-

rata pesakit hidup lapan tahun selepas mengenal pasti Alzheimer, tetapi pesakit dapat

hidup 4 hingga 20 tahun bergantung kepada usia mereka dan keadaan kesihatan yang

lain. Pengesanan awal Alzheimer dan peringkatnya sangat penting, kerana kerana jika

ia semakin buruk ia tidak dapat disembuhkan dan pesakit mempunyai kehidupan yang

sangat dahsyat sebelum kematian mereka. Ramai orang yang kurang berpengetahuan

dan perhatian terhadap isu-isu semasa mengenai penyakit Alzheimer menjadikannya

sukar untuk mengesan penyakit yang dihidapi oleh orang disekeliling mereka..

Mengesan Alzheimer secara awal adalah amat penting kerana kajian menunjukkan

bahawa intervensi dilakukan pada pesakit Alzheimer selepas keputusan peperiksaan

awal untuk menyembuhkan atau sekurang-kurangnya memperlahankannya adalah

sangat berkesan. Semasa pembinaan sistem ini saya menggunakan Model Iteratif

untuk mengumpulkan maklumat dengan membina semula langkah-langkah dalam

Model Iteratif dan untuk mencari penyakit sistem menggunakan pokok Keputusan

untuk mengesan. Di samping itu, sistem ini memberikan peratusan pengesanan

pesakit Alzheimer, jika seseorang positif dibawa ke pakar Alzheimer untuk rawatan,

dan jika tidak perlu melihat pakar Alzheimer. Pokok Keputusan mengklasifikasikan

item data yang diberikan menggunakan nilai atributnya. Pokok keputusan pada

mulanya dibina dari satu set data pra-dikelaskan. Dengan Sistem Pengesanan Awal

Alzheimer, ia dapat membantu keluarga mengesan orang yang mereka sayangi

sebelum terlambat.

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TABLE OF CONTENTS

1.0 BACKGROUND ........................................................................................................... 11

1.1 PROBLEM STATEMENT ............................................................................................ 12

1,2 OBJECTIVE ................................................................................................................. 12

1.3 SCOPE ........................................................................................................................... 13

1.4 LIMITATION OF WORK ............................................................................................. 14

1.5 EXPECTED OUTCOME ............................................................................................. 14

1.6 PROJECT PLANNING ................................................................................................. 15

1.7 REPORT STRUCTURE ................................................................................................ 16

1.8 CHAPTER SUMMARY ................................................................................................ 16

2.1 INTRODUCTION ......................................................................................................... 17

2.1.1 Alzheimer ................................................................................................................ 17

2.1.2 Research on different system that using Decision Tree ......................................... 20

2.1.3Diagnosis of Breast Cancer using Decision Tree Data Mining Technique ............. 20

2.1.4 Lung Cancer Detection using Decision Tree Algorithm ........................................ 21

2.1.5 Intrusion Detection Systems Using Decision Trees................................................ 22

2.2 Comparison Table Between Same Algorithm And Different System ........................... 23

2.3 DECISION TREE ......................................................................................................... 24

2.4 CONCLUSION .............................................................................................................. 27

9

3.1 INTRODUCTION ......................................................................................................... 28

3.2 JUSTIFICATION SELECTION .................................................................................... 28

3.3 METHODOLOGY ........................................................................................................ 29

3.4 SYSTEM REQUIREMENT .......................................................................................... 30

3.4.1 Software Requirement ............................................................................................ 30

3.4.2 Hardware Requirement ........................................................................................... 31

3.5 INTRODUCTION OF SYSTEM MODELLING .......................................................... 32

3.6 FRAMEWORK.............................................................................................................. 32

3.7 CONTEXT DIAGRAM ................................................................................................. 34

3.8 DATA FLOW DIAGRAM ............................................................................................ 35

3.9 DATA FLOW DIAGRAM LEVEL 1 ........................................................................... 39

3.9.1 Manage User For Nurse .......................................................................................... 39

3.9.2 Manage User For Doctor ........................................................................................ 40

3.9.3 Manage User For Patient ........................................................................................ 41

3.9.4 Manage Stage For Nurse ......................................................................................... 42

3.9.5 Manage Questionnaire For Nurse ........................................................................... 43

3.9.6 Manage Result For Patient ...................................................................................... 44

3.9.7 Manage Confirmation For Doctor .......................................................................... 45

3.10 ENTITY RELATIONSHIP DIAGRAM ............................................................... 46

10

4.1 INTRODUCTION ......................................................................................................... 47

4.2 IMPLEMENTATION AND OUTPUT ......................................................................... 47

4.2.1 Database Design...................................................................................................... 47

4.2.2 Interface Design ................................................................................................. 53

4.3 SOLUTION COMPLEXCITY ................................................................................. 60

4.3.1 Questionnaire Sample ........................................................................................ 60

5.1 INTRODUCTION ......................................................................................................... 65

5.2 PROJECT CONTRIBUTION ........................................................................................ 65

5.2 PROJECT CONSTRAINTS AND LIMITATION ........................................................ 65

5.3 FUTURE WORKS......................................................................................................... 66

5.4 CONCLUSION .............................................................................................................. 66

5.0 REFERENCES .......................................................................................................... 67

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CHAPTER I

INTRODUCTION

1.0 BACKGROUND

Brain is the central part of the human nervous system and any abnormality caused by

any disease can lead to complete failure of human structural function. Alzheimer, an

old age disease of people over 65 years causes problems with memory, thinking and

behaviour. This disease progresses very slow and its identification in early stages is

very difficult. It is not a specific disease and the patients may have problems with

memory, communication, concentrated attention, reasoning, judgment, focusing, and

visual perception. The symptoms appear slowly and these gradually will have worse

effects. In its early stages, not only the patients themselves but their loved ones are

Generally unable to accept that the patient is suffering from disease. Alzheimer’s

patients forget the recent information and face challenges in simple arithmetic. They

also have problems in speaking and writing, misplace things and have difficulty in

retracing. Their interest in job and social events lessens and their mood becomes

unpredictable. One can observe visible changes in the personality of the patients. On

average patients live of eight years after identification of Alzheimer, but patients

survive from 4 to 20 years depending on their age and other health conditions

It is the only cause of death that cannot be prevented, cured, or even slowed.

Alzheimer’s disease exacts an enormous toll on individuals, families and healthcare

system. It is a serious problem affecting many aspects of our society. Until

Alzheimer’s disease can be prevented or cured, the impact of this disease will only

continue to intensify[3].

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1.1 PROBLEM STATEMENT

Currently there is no treatment available to slow or stop the deterioration of brain cells

in individuals with Alzheimer’s disease. The symptoms appear slowly and these

gradually will have worse effects. Five drugs are currently approved that temporarily

slow symptom progression. Despite current lack of disease-modifying therapies,

detecting Alzheimer’s disease as early as possible is important as studies show that

Alzheimer’s disease can significantly improve quality of life through all disease

stages. The effects of early action make it easier for families to accepting that the

patient is suffering from disease and giving more support for the patient.

1,2 OBJECTIVE

The objectives of this project have been defined as we can know whether the goals of the

system have been achieved. There are the following objectives that determine the success of

this system:

I. To design the system for people or family to identify the appropriate risks for their

loved one who may have Alzheimer’s disease and make early diagnosis.

II. To develop Early Detection Alzheimer’s disease using Decision Tree Algorithm to

check the possibility early detection based on symptoms stated.

III. To test the proposed system is functionality and beneficially to users.

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1.3 SCOPE

The scope of the system basically means everything that will be covered in the research

project and who involves in it. It defines clearly the extent of content that will be covered by

the whole system. The scope of the study has to be defined at a preliminary stage and that is

very important.

User/Patient

I. Register

II. Manage profile.

III. Fill the questionnaire of Alzheimer’s disease.

IV. Check the health status on Alzheimer’s disease.

V. Generate report status of Alzheimer’s disease.

VI. View result Alzheimer’s disease status.

Nurse

I. Register

II. Manage profile.

III. Manage Alzheimer’s symptom.

IV. View Alzheimer’s symptom Report.

Doctor

I. Register

II. Manage profile.

III. View result patients.

IV. Confirmed the stage of the Alzheimer’s disease.

V. Generate report.

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1.4 LIMITATION OF WORK

The system will focus on probability early detection of Alzheimer’s disease based on

symptom. The system does not include diagnosing the patient but the percentage of

Alzheimer’s disease probability results is taken from diagnosing and entering into the

system. This system is developing on web based so that it only can be open using a

web browser not in android or iOS application.

1.5 EXPECTED OUTCOME

This system is expected to be implemented in web-based. In addition, this system can

Detect Early Alzheimer’s Disease Detection using Decision Tree to check the

possibility of early detection based on the stated symptoms. So that the system will

help families identify appropriate risks for their loved one who may have Alzheimer’s

disease and make early diagnosis. Additionally, this system also saves time to make a

preliminary check only through the system does not have to queue up for inspection.

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1.6 PROJECT PLANNING

Table 1 Shows Gantt Chart of schedule and planning for this project proposal.

Task / Month

January Febuary March April

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Project Title

Discussion and

Briefing

Project Title

Registration

Proposal Writing

(introduction)

Proposal Writing

(literature review-

1)

Proposal Writing

(literature review-

2)

Proposal Progress

Presentation and

Evaluation

Discussion and

Correction of

the Proposal

Proposed Solution

–

Methodology – 1

Proposed Solution

–

Methodology – 2

Proof of Concept

Drafting Report

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1.7 REPORT STRUCTURE

The first chapter of this report is the introduction to the projects which includes introduction,

problem statement, objective, scope, limitation of works and planning for this project. The

overall logic of the system is stated here. The second chapter is literature review. This chapter

provide better understanding based on the explanation of related research done in the related

field. Third chapter describe the methodology used in this system. It discuss project

methodology and requirement of software and hardware that guide the system development,

it deals with project design and modelling which the core part in the development process.

The data flow diagram and the context diagram for this system is shown. Entity relationship

diagram is also included to provide better understanding on database design. Fourth chapter

will explain the function and flow of the system with interfaces provided, and a few tests are

done. In the last chapter which is conclusion, the result has been discussed, concluded and

summarised.

1.8 CHAPTER SUMMARY

This chapter basically deliver the early stages about this project development. It explains

more about the initial project development process.

Proposal

Submit draf report

- supervisor

Report Correction

Seminar

Presentation

Final Report

Submission

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CHAPTER II

LITERATURE REVIEW

2.1 INTRODUCTION

The purpose of this chapter is to present selected literature review, which is very important

for the research. This chapter also describes and explains of the literature review carried out

on the system that will be used as references in developing this system. Previous research or

existing system will also be discussed in this section. Literature review aim to review the

critical points of the current knowledge on a particular topic. Therefore, the purpose of the

literature review is to find, read and analyse the literature or any works or studies related to

this system. It is important to well understand about all information to be considered and

related before developing this system. For this project, some research has been done to

understand about Alzheimer’s disease and technique that had been choosing to implement in

the system.

2.1.1 Alzheimer

Alzheimer's is a type of dementia that causes problems with memory, thinking and

behaviour. Symptoms usually develop slowly and get worse over time, becoming severe

enough to interfere with daily tasks.Alzheimer's is not a normal part of aging. The greatest

known risk factor is increasing age, and the majority of people with Alzheimer's are 65 and

older. But Alzheimer's is not just a disease of old age. Approximately 200,000 Americans

under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset

Alzheimer’s)[3].

Alzheimer's worsens over time .Those with Alzheimer's live an average of eight years

after their symptoms become noticeable to others, but survival can range from four to 20

years, depending on age and other health conditions[3].

Alzheimer's disease typically progresses slowly in three general stages — mild (early-

stage), moderate (middle-stage), and severe (late-stage). Since Alzheimer's affects people in

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different ways, each person will experience symptoms - or progress through Alzheimer's

stages - differently.[4]

The stages below provide an overall idea of how abilities change once symptoms

appear and should only be used as a general guide. They are separated into three different

categories: mild Alzheimer's disease, moderate Alzheimer's disease and severe Alzheimer's

disease. Be aware that it may be difficult to place a person with Alzheimer's in a specific

stage as stages may overlap[4].

2.1.1.1 Mild Alzheimer's disease (early-stage)

In the early stage of Alzheimer's, a person may function independently. He or she may still

drive, work and be part of social activities. Despite this, the person may feel as if he or she is

having memory lapses, such as forgetting familiar words or the location of everyday

objects[5].

Friends, family or others close to the individual begin to notice difficulties. During a detailed

medical interview, doctors may be able to detect problems in memory or concentration.

Common difficulties include[4]:

Problems coming up with the right word or name

Trouble remembering names when introduced to new people

Challenges performing tasks in social or work settings.

Forgetting material that one has just read

Losing or misplacing a valuable object

Increasing trouble with planning or organizing

2.1.1.2 Moderate Alzheimer's disease (middle-stage)

Moderate Alzheimer's is typically the longest stage and can last for many years. As the

disease progresses, the person with Alzheimer's will require a greater level of care[4].

You may notice the person with Alzheimer's confusing words, getting frustrated or angry, or

acting in unexpected ways, such as refusing to bathe. Damage to nerve cells in the brain can

make it difficult to express thoughts and perform routine tasks[4].

19

At this point, symptoms will be noticeable to others and may include:

Forgetfulness of events or about one's own personal history

Feeling moody or withdrawn, especially in socially or mentally challenging situations

Being unable to recall their own address or telephone number or the high school or

college from which they graduated

Confusion about where they are or what day it is

The need for help choosing proper clothing for the season or the occasion

Trouble controlling bladder and bowels in some individuals

Changes in sleep patterns, such as sleeping during the day and becoming restless at

night

An increased risk of wandering and becoming lost

Personality and behavioural changes, including suspiciousness and delusions or

compulsive, repetitive behaviour like hand-wringing or tissue shredding

2.1.1.3 Severe Alzheimer's disease (late-stage)

In the final stage of this disease, individuals lose the ability to respond to their environment,

to carry on a conversation and, eventually, to control movement. They may still say words or

phrases, but communicating pain becomes difficult. As memory and cognitive skills continue

to worsen, significant personality changes may take place and individuals need extensive help

with daily activities[4].

At this stage, individuals may:

Need round-the-clock assistance with daily activities and personal care

Lose awareness of recent experiences as well as of their surroundings

Experience changes in physical abilities, including the ability to walk, sit and,

eventually, swallow

Have increasing difficulty communicating

Become vulnerable to infections, especially pneumonia

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2.1.2 Research on different system that using Decision Tree

A few researches on different journal that used Decision Tree are used to compared

which technique that suit with the complexity of the system depends on the problem

statement stated

2.1.3Diagnosis of Breast Cancer using Decision Tree Data Mining Technique

This paper presents a decision tree based data mining technique for early detection of

breast cancer. Breast cancer diagnosis differentiates benign (lacks ability to invade

neighbouring tissue) from malignant (ability to invade neighbouring tissue) breast tumours.

This paper also discusses various data mining approaches that have been utilized for breast

cancer diagnosis, and also summarizes breast cancer in general (types, risk factors, symptoms

and treatment). Data mining techniques tends to simplify the prediction segment. Decision

tree is a classifier that is expressed as a recursive partition of the instance space. It creates a

predictive model, which maps observations about a node to conclusions about the nodes’

target value. Decision tree provides a powerful technique for classification and prediction in

Breast Cancer diagnosis problem. In this paper we have chosen J48 decision tree algorithm to

establish the model.[1]

The tree generated by J48 can be used for classification of whether a patient had

benign or malignant tumour. The data mining technique uses the concept of information

entropy. Each attribute of the data is used to make a decision by splitting the data into smaller

modules. It examines normalized information gain (IG) (difference in entropy) those results

from choosing an attribute as a split point. The highest normalized IG is used at the root of

the tree. The procedure is repeated until the leaf node is created for the tree specifying the

class attribute that is chosen[1].

.

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2.1.4 Lung Cancer Detection using Decision Tree Algorithm

Lung cancer, also known as lung carcinoma a malignant lung tumour characterized by

uncontrolled cell growth in tissues of the lung. If left untreated, this growth can spread

beyond the lung by the process of metastasis into nearby tissue or other parts of the body.

Most cancers that start in the lung, known as primary lung cancers, are carcinomas. The

two main types are small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma

(NSCLC).Cigarette smoking is the principal risk factor for development of lung cancer. A

Few popular technique are used to Detect the lungs cancer like support vector machine.

(SVM), naive bayes classifier. A new approach to detect the lungs cancer by Decision

tree algorithm will provide effective result as compare to other algorithm. The proposed

system will enhance the performance of prediction and classification[2].

A decision tree is a decision support tool that uses a tree-like graph or model of

decisions and their possible consequences, including chance event outcomes, resource

costs, and utility. It is one way to display an algorithm. A decision tree is a flowchart-like

structure in which each internal node represents a "test" on an attribute (e.g. whether a

coin flip comes up heads or tails), each branch represents the outcome of the test and each

leaf node represents a class label (decision taken after computing all attributes). The paths

from root to leaf represent classification rules. The Data comes into the database is of

training data , through which the system is trained[2].

To make proper decision on lung cancer Decision tree algorithm is applied on

available data to get system train and ready to take decision for unknown data.Once

Decision tree algorithm is applied on training data, it generates an tree like structure

based on data available in training database. Splitting and aggregation of data is done

while decision tree is generating[2].

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2.1.5 Intrusion Detection Systems Using Decision Trees

Security of computers and the networks that connect them is increasingly becoming of

great significance. Intrusion detection is a mechanism of providing security to computer

networks. Although there are some existing mechanisms for Intrusion detection, there is need

to improve the performance. Data mining techniques are a new approach for Intrusion

detection. In this paper we investigate and evaluate the decision tree data mining techniques

as an intrusion detection mechanism and we compare it with Support Vector Machines

(SVM). Intrusion detection with Decision trees and SVM were tested with benchmark 1998

The Defence Advanced Research Projects Agency (DARPA) Intrusion Detection data set.

Our research shows that Decision trees gives better overall performance than the SVM.

Decision tree induction is one of the classification algorithms in data mining. The

Classification algorithm is inductively learned to construct a model from the reclassified data

set. Each data item is defined by values of the attributes. Classification may be viewed as

mapping from a set of attributes to a class. The Decision tree classifies the given data item

using the values of its attributes. The decision tree is initially constructed from a set of pre-

classified data. The main approach is to select the attributes, which best divides the data items

into their classes. According to the values of these attributes the data items are partitioned.

This process is recursively applied to each partitioned subset of the data items. The process

terminates when all the data items in current subset belongs to the same class. A node of a

decision tree specifies an attribute by which the data is to be partitioned. Each node has a

number of edges, which are labelled according to a possible value of the attribute in the

parent node. An edge connects either two nodes or a node and a leaf. Leaves are labelled with

a decision value for categorization of the data[3].

Induction of the decision tree uses the training data, which is described in terms of the

attributes. The main problem here is deciding the attribute, which will best partition the data

into various classes. The Iterative Dichotomiser 3 (ID3) algorithm uses the information

theoretic approach to solve this problem. Information theory uses the concept of entropy,

which measures the impurity of a data items. The value of entropy is small when the class

distribution is uneven, that is when all the data items belong to one class. The entropy value

is higher when the class distribution is more even, that is when the data items have more

classes. Information gain is a measure on the utility of each attribute in classifying the data

items. It is measured using the entropy value. Information gain measures the decrease of the

23

weighted average impurity (entropy) of the attributes compared with the impurity of the

complete set of data items. Therefore, the attributes with the largest information gain are

considered as the most useful for classifying the data items[3].

2.2 Comparison Table Between Same Algorithm And Different System

Table 2-1: Shows the comparison between same Decision Tree algorithm and different

system.

Author/Journa

l

/Year

Author/Year

System Name Method Description Advantages

Ronak Sumbaly,

N. Vishnusri, S.

Jeyalatha

Department of

Computer

Science BITS,

Pilani – Dubai

United Arab

Emirates..

International

Journal of

Computer

Applications

(0975 – 8887)

Volume 98–

No.10, July

2014

Diagnosis of

Breast Cancer

using Decision

Tree Data

Mining

Technique

Decision

Tree

This paper presents a

decision tree based data

mining technique for

early detection of

breast cancer. Breast

cancer diagnosis

differentiates benign

(lacks ability to invade

neighbouring tissue)

from malignant (ability

to invade neighbouring

tissue) breast tumours.

This paper also

discusses various data

mining approaches that

have been utilized for

breast cancer diagnosis,

and also summarizes

breast cancer in general

(types, risk factors,

symptoms and

treatment).

Experimental

results show the

effectiveness of

the proposed

model and

shows the

accuracy

measures of the

result

Ms. Leena Patil,

Ms. Aparna

Sirsat, Ms.

Diksha Kamble,

Mr.Yogesh

Pawar [12]

Lung Cancer

Detection

using Decision

Tree

Algorithm

Decision

Tree

A new approach to

detect the lungs cancer

by Decision tree

algorithm will provide

effective result as

compare to other

algorithm. The

proposed system will

To make proper

decision on lung

cancer Decision

tree algorithm is

applied on

available data to

get system train

and ready to

24

enhance the

performance of

prediction and

classification.

take decision for

unknown data.

Sandhya

Peddabachigari,

Ajith

Abraham*,

Johnson

Thomas

[10]

Intrusion

Detection

Systems Using

Decision Trees

Decision

Tree

To classify an unknown

object, one starts at the

root of the decision tree

and

follows the branch

indicated by the

outcome of each test

until a leaf node is

reached. The

name of the class at the

leaf node is the

resulting classification.

select the

attributes, which

best divides the

data items into

their classes

Table 2-1 : Shows the comparison between the related journals with different System

2.3 DECISION TREE

In a tree structure leaves represent the class labels and branches represent

conjunctions of feature leading to the class labels. Figure 9 shows the illustrated example of

binary decision tree[13].

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Steps of Construct a Decision-Tree

There are few steps for construction of Decision tree:

1. First step is check whether all the cases belong to the same class and if Yes then tree is a

leaf and that node is labelled by that class.

2. Entropy and information gain are calculated for each and every attribute

3. Assume best selection criteria and accordingly consider the splitting attribute.

4. Counting the information gain: The concept of entropy arrives in this part. Entropy can be

stated as its measure of any disordered in the data. Entropy can also be called as a

measurement of uncertainty in any random variable.

5. Pruning: For the tree creation process, pruning is an important technique to be performed.

The dataset may sometimes contain subsets that are not well defined of instances, so for

classification of such a subsets, Pruning can be used [4].

6. Pruning has two types:

i. Post Pruning: This type of Pruning is performed after the creation of tree.

ii. Online Pruning: This type of Pruning is performed during the process of tree

creation.

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5.3 Formulas for Entropy Calculation

Entropy= - p(a)*log(p(a)) – p(b)*log(p(b))

P(a) and P(b) is the probability of class (a) and (b) Compute it as the proportion of

class a&b in the set.

Information Gain=entropy (after)- entropy (before)

Probability of class: [No of instances of particular class/ Total no of instances]

Example->

ends – vowel

[9m,5f]

Notation reprents the class distribution of

/ \

Instances that reached a node

=0 =0

-------- ----------

[3m,4f] [6m,1f]

As you can see, before the split we had 9 males and 5 females, i.e.

P(m)=9/14 and P(f)=5/14. According to the definition of entropy:

Entropy before = -P(f) * log2 p(f) – p(m) log2 p(m)

Entropy before = - (5/14) * log2(5/14) -(9/14) * log2 (9/14) = 0.9403

Next we compare it with the entropy computed after considering the

split by looking at two child branches. In the left branch of ends-vowel=1, we

have:

Entropy left= - (3/7) * log2 (3/7) – (4/7) * log2 (4/7) =0.9852

t = - (6/7) * log2 (6/7) - (1/7) * log2 (1/7)=0.5917

We combine the left/right entropies using the number of instances

down each branch as weight factor (7 instances went left, and 7 instances went

right), and get the final entropy after the split:

Entropy after = 7/14 * entropy left + 7/14* entropy right=0.7885

Now by comparing the entropy before and after the split, we obtain a

measure of information gain, or how much information we gained by doing

the split using that particular feature:

Information Gain = [Entropy before-Entropy after]=0.1518

27

2.4 CONCLUSION

This chapter discusses literature review that had been reviewed during feasibility

studies. The literature review helps developer to discover the problem of previous research or

system which needs to be improves and overcome in this system development. Furthermore,

it also helps to gain understanding about the system that undergo the development process.

As a conclusion, Decision Tree Algorithm is the most suitable method to use in

developing the system. Decision Tree Algorithm can store all the rules in „working memory‟

which means all the requirements to get successfully get into hostel placement can be stored

in Decision Tree Algorithm.

28

CHAPTER III

METHODOLOGY

3.1 INTRODUCTION

In this chapter, it will focused on the methodology that being applied in the

software development. The methodology of software development is the method in

managing project development. There are many model of the methodology are

available such as Waterfall model,V model, Incremental model, RAD model, Agile

model ,Iterative model and Spiral model . However, it still need to be consider by

developer to decide which is will be used in the project. The methodology model is

useful to manage the project efficiently and able to help developer from getting any

problem during time of development. Also, it help to achieve the objective and scope

of the projects. In order to build the project, it need to understand the stakeholder

requirements.

3.2 JUSTIFICATION SELECTION

For this project, we purpose Iterative Model as the model of the methodology, which has

been widely applied in the other project. It is because of few reasons. Iterative Model is a

particular implementation of a software development life cycle (SDLC) that focuses on an

initial, simplified implementation, which then progressively gains more complexity and a

broader feature set until the final system is complete[10]. There are many advantage of using

Iterative model. Primary advantage of the iterative model is the ability to rapidly adapt to the

ever-changing needs of both the project or the whims of the client. Even fundamental

changes to the underlying code structure or implementations (such as a new database system

or service implementation) can typically be made within a minimal time frame and at a

reasonable cost, because any detrimental changes can be recognized and reverted within a

short time frame back to a previous iteration. The iterative model is best thought of as a

cyclical process. There are five phases that involved in the iterative model that including

Planning & Requirements, Analysis & Design, Implementation, Testing and Evaluation[11].

For each phase, there are activities are involved. In 3.3 section, there is explanation of the

activity of each phase[10].

29

Figure 3.1 below shown that the planning phase as the start and evaluation as last phase.

Figure 3.1:Iterative Model

3.3 METHODOLOGY

In the Early Alzheimer’s Disease Detection System, Iterative model has been chosen as the

methodology .There are Five phases that involve in the iterative model[11]:

1) Planning & Requirements:

The first step is go through an initial planning stage to map out the specification documents,

establish software or hardware requirements, and generally prepare for the upcoming stages

of the cycle. The project has been discussed with project supervisor. From that

discussion, Early Alzheimer’s Disease Detection System has been proposed. The requirement

and risk was assessed after doing study on existing system and do literature review about

another existing research[11].

2) Analysis & Design:

Once planning is complete, an analysis is performed to nail down the appropriate business

logic, database models, and the like that will be required at this stage in the project. The

30

design stage also occurs here, establishing any technical requirements (languages, data layers,

services, etc) that will be utilized in order to meet the needs of the analysis stage.

3) Implementation[11]:

With the planning and analysis out of the way, the actual implementation and coding process

can now begin. All planning, specification, and design docs up to this point are coded and

implemented into this initial iteration of the project[11].

4) Testing:

Once this current build iteration has been coded and implemented, the next step is to go

through a series of testing procedures to identify and locate any potential bugs or issues that

have have cropped up[11].

5) Evaluation:

Once all prior stages have been completed, it is time for a thorough evaluation of

development up to this stage. This allows the entire team, as well as clients or other outside

parties, to examine where the project is at, where it needs to be, what can or should change,

and so on[11].

3.4 SYSTEM REQUIREMENT

Based on techopedia.com, the implementation that the system needed to make sure the

hardware or software can be run smoothly. If not success in fulling the requirement,

the failure of performance and installation may occur.

3.4.1 Software Requirement

The software requirements are needed to build system are:

31

Table 3.1: List of Software

SOFTWARE

DESCRIPTION

PHP Programming Language for writing a coding

of this system

XAMPP Server MySQL

Using this software to create database and

manipulate database and connect database

with PHP.

Edraw Max Create and design Data Flow Diagram

and Context Diagram

Dropbox Save and update the document for this

system and also as the backup file.

Baidu Browser Medium to find reference to do system

and as medium to system be display and

run.

Notepad++ As medium to write PHP coding to build

system.

Microsoft Word Write the documentation

3.4.2 Hardware Requirement

The hardware requirements to build the system are:

Table 3.2: List of Hardware

HARDWARE

DESCRIPTION

1) Laptop LENOVO G40

Intel(R) Core(TM) i5-5200U CPU @ 2.20GHz

2.20GHz

4.00GB RAM

Window 8 operating system

64 bit Operating system,

32

3.5 INTRODUCTION OF SYSTEM MODELLING

By Kast and Rosenzweig, system is organized and complicated one. So, system modelling

able to assist analyst be capable in understanding functionality and models of their system to

present the system to stakeholders. Systems are presented in different models which are

created from different perspectives. There are three perceptive such as external, behavior and

structural. Examples of model are Framework, context diagram, Data Flow Diagrams (DFD)

and Entity Relationship Diagram (ERD). DFD are modelling the system from functional

aspects. It also can show the flow of data between systems. While Entity Relationship

Diagram are used to describe the relationship between entities and attributes of entities. It

widely available in database modelling. Next, another explanation will be available in this

chapter.

3.6 FRAMEWORK

Framework is basic structures that are needed to solve the complex problem or as known as

the tools and material or component. In the Early Alzheimer’s Disease Detection System,

there are three users that we called it as Doctor, Nurse and Patient.

For Doctor, they need log into the system if they want manage their system. After

login, they are retrieved into their own interface (different interface with user interface)

.They can manage profile, manage patient/user’s profile, confirmed the stage of the

Alzheimer’s disease and generate report.

For Nurse, they need log into the system if they want manage their system. After

login, they are retrieved into their own interface (different interface with user interface)

.They can manage profile, manage patient/user’s profile, add, delete and update Alzheimer’s

symptom.

While for Patient, they need register firstly to gain PatientID , email and password. The

PatientID, user Name and password will be used by them to log into the system. After

successfully login, they can use Early Alzheimer’s Disease Detection System by answer the

questionnaire that given. With the answer, the system will generate the result about the user’s

potential to get Alzheimer and they will advise to seek doctors’ confirmation to find out real

results. They also can view information about Alzheimer’s disease.

33

Figure 3.2:Framework for Diabetes Prediction System.

34

3.7 CONTEXT DIAGRAM

Figure 3.3 show the Context Diagram for Early Alzheimer’s Disease Detection System.

There are three actor are involved in this system which is Patient, Doctor and Nurse. In

context diagram, the flow of the actors are explained and their ability in this system.

Figure 3.3 :Context Diagram

Description of Context Diagram

Based on figure 3.3, the ALZHEIMER’S DISEASE DETECTION SYSTEM process at the

centre of figure. There are three entities or actors are available are PATIENT, DOCTOR and

NURSE. There are twelve data flows in the Context Diagram. Only two outgoing data flow

from DOCTOR which consist of DOCTOR’S DETAIL and ALZHEIMER’S DISEASE

CONFIRMATION STATUS. While from PATIENT, there also two outgoing data flow

which consist of PATIENT’S DETAILS and QUESTIONNAIRE DETAIL. For NURSE,

there are also three outgoing data flow which consist of NURSE’S DETAIL ,

QUESTIONNAIRE DETAILS AND ALZHEIMER STAGE DETAILS. For ingoing data

flow, DOCTOR have only one ingoing data which is QUESTIONNAIRE INFORMATION.

For ingoing data flow, DOCTOR have only have two which is QUESTIONNAIRE

INFORMATION and REPORT STATUS. PATIENT also has two ingoing data flow which is

ALZHEIMER’S DISEASE CONFIRMATION STATUS and REPORT STATUS.

0

Early Alzheimer's

Disease Detection

System Using Decision

Tree Algorithm

PATIENT

Patient's Detail

Questionnaire Detail

Alzheirmer's Disease Confirmation Status

Report StatusDOCTOR

Report Status

Questionnaire Information

Doctor's Detail

Alzheimer's Disease Confirmation Status

NURSE

Nurs

e's

Deta

ils

Alz

heim

er S

tage D

eta

ils

Questio

nnaire

Deta

ils

Questio

nnaire

Info

rmatio

n

35

3.8 DATA FLOW DIAGRAM

Figure 3.4 show the Data Flow Diagram level 0 for the Early Alzheimer’s Disease Detection

System. Since the figure 3.4 has been explained the flow of the actors; Patient, Doctor and

Nurse, in this chapter, the more details about the flow are explained with DFD

LEVEL 0 and following by DFD LEVEL 1. The functionality for each process also

will be described and able to help developer to understand their system.

36

Figure 3.4 Data Flow Diagram Level 0 Early Alzheimer’s Disease Detection System

1.0

Register

2.0

Manage User

3.0

manage

Alzheimer

Stage

4.0

Manage

Questionnaire

5.0

Manage Result

6.0

Manage

Alzheimer

confirmation

7.0

Report

Nurse

Doctor

Patient

D1 Nurse

D2 Doctor

D3 Patient

D4 Stage

D5 Questionnaire

D6 Result

Nurse Detail Nurse Information

Nurse Detail

Nurse Information

Alzheimer Stage Details Alzheimer Stage Information

Alzheimer Stage Information

Questionnaire Details

Questionnaire

Information

Doctor InformationDoctor Detail

Doctor Detail Doctor Information

Alzheimer Confirmation Alzheimer Confirmation Status

Alzheimer Confirmation Status

Answering Details Questionnaire result

Questionnaire result

Patient Detail Patient Information

Patient Detail Patient Information

Alzheimer Confirmation Status

Questionnaire Information

Report Status

Report Status

Report Information

Report Status

37

Description of Data Flow Diagram level 0

There are three entities which are PATIENT, NURSE and DOCTOR. While there are

seven processes are identified such as REGISTRATION, MANAGE USER, MANAGE

STAGE, MANAGE QUESTIONNAIRE, MANAGE RESULT, MANAGE

CONFIRMANTION and lastly, REPORT. Next, NURSE, DOCTOR, PATIENT, STAGE,

QUESTIONNAIR and RESULT are the seven data stores for Early Alzheimer’s Disease

Detection System.

1) NURSE input NURSE DETAILS into REGISTRATION which output is

NURSE INFORMATION into NURSE data store.

2) NURSE input NURSE DETAIL into MANAGE USER which output is

NURSE INFORMATION into NURSE data store.

3) NURSE input ALZHEIMER’S STAGE DETAIL into MANAGE

ALZHEIMER STAGE which output is ALZHEIMER’S STAGE

INFORMATION into STAGE data store and invoke ALZHEIMER’S STAGE

INFORMATION input into MANAGE QUESTIONNAIRE which output

QUESTIONNAIRE INFORMATION to QUESTIONNAIRE data store

4) NURSE input ALZHEIMER’S STAGE DETAIL into MANAGE

ALZHEIMER STAGE which output is ALZHEIMER’S STAGE

INFORMATION into STAGE data store and invoke ALZHEIMER’S STAGE

INFORMATION input into MANAGE QUESTIONNAIRE which output

QUESTIONNAIRE INFORMATION to QUESTIONNAIRE data store

5) NURSE input QUESTIONNAIRE DETAIL into MANAGE

QUESTIONNAIRE which output is QUESTIONNAIRE INFORMATION

into QUESTIONNAIRE data store and invoke QUESTIONNAIRE

INFORMATION input into MANAGE RESULT which output

QUESTIONNAIRE RESULT to RESULT data store

6) DOCTOR input DOCTOR DETAILS into REGISTRATION which output is

DOCTOR INFORMATION into DOCTOR data store.

7) DOCTOR input DOCTOR DETAIL into MANAGE USER which output is

DOCTOR INFORMATION into DOCTOR data store.

8) DOCTOR input ALZHEIMER CONFIRMATIONN into MANAGE

ALZHEIMER CONFIRMATION which output is ALZHEIMER

38

CONFIRMATION STATUS into RESULT data store and invoke

ALZHEIMER CONFIRMATION STATUS into PATIENT.

9) PATIENT input PATIENT DETAILS into REGISTRATION which output is

PATIENT INFORMATION into PATIENT data store.

10) PATIENT input PATIENT DETAIL into MANAGE USER which output is

PATIENT INFORMATION into PATIENT data store.

11) PATIENT input QUESTIONNAIRE DETAILS into MANAGE RESULT

which output is QUESTIONNAIRE RESULT into RESULT data store and

invoke QUESTIONNAIRE RESULT input into MANAGE ALZHEIMER

CONFIRMATION which output ALZHEIMER CONFIRMATION STATUS

to RESULT data store and invoke ALZHEIMER CONFIRMATION STATUS

input into PATIENT.

12) All entities and data stores will input the REPORT into REPORT which is

output is REPORT

39

3.9 DATA FLOW DIAGRAM LEVEL 1

3.9.1 Manage User For Nurse

Figure 3.5: Data Flow Diagram Level 1 for Manage User For Nurse

Description :

1. An NURSE input NURSE DETAIL to LOGIN process and then the process send

NURSE INFORMATION into NURSE data store.

2. An NURSE input NURSE DETAIL to ADD USER DETAIL process and then the

process send NURSE INFORMATION into NURSE data store.

3. An NURSE input NURSE DETAIL to UPDATE USER DETAIL process and then

the process send NURSE INFORMATION into NURSE data store.

4. An NURSE input NURSE DETAIL to DELETE USER DETAIL process and then the

process send NURSE INFORMATION into NURSE data store.

2.1

Login

2.2

Add User

Detail

2.3

Update User

Detail

2.4

Delete User

Detail

NurseD1 Nurse

Nurse Details

Nurse Details

Nurse Details

Nurse Details

Nurse Information

Nurse Information

Nurse Information

Nurse Information

40

3.9.2 Manage User For Doctor

Figure 3.6: Data Flow Diagram Level 1 for Manage User For Doctor

Description :

1. An DOCTOR input DOCTOR DETAIL to LOGIN process and then the process send

DOCTOR INFORMATION into DOCTOR data store.

2. An DOCTOR input DOCTOR DETAIL to ADD USER DETAIL process and then the

process send DOCTOR INFORMATION into DOCTOR data store.

3. An DOCTOR input DOCTOR DETAIL to UPDATE USER DETAIL process and

then the process send DOCTOR INFORMATION into DOCTOR data store.

4. An DOCTOR input DOCTOR DETAIL to DELETE USER DETAIL process and

then the process send DOCTOR INFORMATION into DOCTOR data store.

2.1

Login

2.2

Add User

Detail

2.3

Update User

Detail

2.4

Delete User

Detail

DoctorD2 Doctor

Doctor Details

Doctor Details

Doctor Details

Doctor Details

Doctor Information

Doctor Information

Doctor Information

Doctor Information

41

3.9.3 Manage User For Patient

Figure 3.7: Data Flow Diagram Level 1 for Manage User For Patient

Description :

1. An PATIENT input PATIENT DETAIL to LOGIN process and then the process send

PATIENT INFORMATION into PATIENT data store.

2. An PATIENT input PATIENT DETAIL to ADD USER DETAIL process and then

the process send PATIENT INFORMATION into PATIENT data store.

3. An PATIENT input PATIENT DETAIL to UPDATE USER DETAIL process and

then the process send PATIENT INFORMATION into PATIENT data store.

4. An PATIENT input PATIENT DETAIL to DELETE USER DETAIL process and

then the process send PATIENT INFORMATION into PATIENT data store.

2.1

Login

2.2

Add User

Detail

2.3

Update User

Detail

2.4

Delete User

Detail

PatientD3 Patient

Patient Details

Patient Details

Patient Details

Patient Details

Patient Information

Patient Information

Patient Information

Patient Information

42

3.9.4 Manage Stage For Nurse

Figure 3.8: Data Flow Diagram Level 1 for Manage Stage For Nurse

Description :

1. An NURSE input ALZHEIMER STAGE DETAIL to ADD ALZHEIMER STAGE

process and then the process send ALZHEIMER STAGE INFORMATION into

STAGE data store.

2. An NURSE input ALZHEIMER STAGE DETAIL to UPDATE ALZHEIMER

STAGE process and then the process send ALZHEIMER STAGE INFORMATION

into STAGE data store.

3. An NURSE input ALZHEIMER STAGE DETAIL to DELETE ALZHEIMER

STAGE process and then the process send ALZHEIMER STAGE INFORMATION

into STAGE data store.

3.1

Add Alzheimer

Stage

3.2

Update

Alzheimer

Stage

3.3

Delete

Alzheimer

Stage

Nurse

D4 Stage

Alzheimer Stage Details Alzheimer Stage Information

Alzheimer Stage

Details

Alzheimer Stage DetailsAlzheimer Stage Information

Alzheimer Stage

Information

43

3.9.5 Manage Questionnaire For Nurse

Figure 3.9: Data Flow Diagram Level 1 for Manage Questionnaire For Nurse

Description :

1. An NURSE input QUESTIONNAIRE DETAIL to ADD QUESTIONNAIRE process

and then the process send QUESTIONNAIRE INFORMATION into

QUESTIONNAIRE data store and invokes STAGE INFORMATION into ADD

QUESTIONNAIRE.

2. An NURSE input QUESTIONNAIRE DETAIL to UPDATE QUESTIONNAIRE

process and then the process send QUESTIONNAIRE INFORMATION into

QUESTIONNAIRE data store and invokes STAGE INFORMATION into UPDATE

QUESTIONNAIRE.

3. An NURSE input QUESTIONNAIRE DETAIL to DELETE QUESTIONNAIRE

process and then the process send QUESTIONNAIRE INFORMATION into

QUESTIONNAIRE data store and invokes STAGE INFORMATION into DELETE

QUESTIONNAIRE.

4.1

Add

Questionnaire

4.2

Update

Questionnaire

4.3

Delete

Questionnaire

Nurse

D4 Stage

D5 Questionnaire

Questionnaire Details

Questionnaire Details

Questionnaire Details

Questionnaire Information

Questionnaire Information

Questionnaire Information

Stage Information

Stage Information

Stage Information

44

3.9.6 Manage Result For Patient

Figure 3.10: Data Flow Diagram Level 1 for Manage Result For Patient

Description :

1. An PATIENT input ANSWER QUESTIONNAIRE to ANSWER QUESTIONNAIRE

process and then the process send QUESTIONNAIRE INFORMATION into

QUESTIONNAIRE data store.

2. An QUESTIONNAIRE data store input QUESTIONNAIRE INFORMATION into

GET RESULT process and then ,the process retrieve QUESTIONNAIRE

INFORMATION to RESULT data store.

3. RESULT data store then input RESULT GENERATED into GET RESULT process

which is output RESULT to PATIENT

5.1

Answer

Questionnaire

5.2

Get Result

Patient

D5 Questionnaire

D6 Result

Answer Questionnaire

Result

Generate result

Questionnaire Information

Questionnaire

Information

Questionnaire

Information

45

3.9.7 Manage Confirmation For Doctor

Figure 3.11: Data Flow Diagram Level 1 for Manage Confirmation For Doctor

Description :

1. An RESULT data store input CONFIRMATION OF RESULT into GIVE

CONFIRMATION process An DOCTOR input DOCTOR’S CONFIRMATION to

GIVE CONFIRMATION process and then the process send CONFIRMATION OF

RESULT into RESULT data store.

2. A RESULT data store input CONFIRMATION OF RESULT into GET

CONFIRMATION process and then ,the process send CONFIRMATION OF

RESULT to PATIENT

6.1

Give

Confirmation

6.2

Get

Confirmation

Doctor

D6 Result

Patient

Doctor

ConfirmationConfirmation of Result

Result Status

Confirmation of Result

Confirmation of

Result

46

3.10 ENTITY RELATIONSHIP DIAGRAM

Figure 3.12 :Entity Relationship Diagram of Early Alzheimer’s Disease Detection System

(one to many) strong relationship

(one to many) weak relationship

An entity-relationship diagram (ERD) show that the entities information and entities

relationship. ERD is consist of identifying and defining the entities, determine entities

interaction and the cardinality of the relationship.

47

CHAPTER IV

CHAPTER IMPLEMENTATION AND TESTING

4.1 INTRODUCTION

In this chapter, the implementation of the system will described how the system’s output and

input during the testing phase. Testing are need to avoid any error occur in the future. Early

testing can help in maintaining the system’s performance. In this chapter also, interface of the

system will help the user to understanding the system. Interface is built through the

specifications and requirement in make sure it achieves the objectives.

4.2 IMPLEMENTATION AND OUTPUT

4.2.1 Database Design

Database playing important part in making the data and information in the system display

properly. Database is used to store the data.

48

4.2.1.1 Diabetes Prediction System Database.

Figure 4.1: The tables in the database Diabetes Prediction System.

There are seven table available in the database such as doctor, login , nurse, patient,

questionnaire, result and stage. For each table, there are attributes at every column.

4.2.1.2 Table doctor

Figure 4.2 : Table doctor

Table doctor contain DoctorID, Name, PhoneNo, Email and Address. In this table, DoctorID is a

primary key and not null.

49

4.2.1.3 Table login

Figure 4.3 : Table login

Table login contain UserID, and Password. In this table, UserID is a primary key and not null.

4.2.1.4 Table nurse

Figure 4.4 : Table nurse

Table nurse contain NurseID, Name, PhoneNo, Email and Address. In this table, NurseID is a

primary key and not null.

50

4.2.1.5 Table patient

Figure 4.5: Table patient

Table patient contain PatientID, PatientName, PatientIcNo, PatientYear, PhoneNo,

Address,GuardianName, GuardianPhoneNo ,Relation, GuardianAddress and Email. In this

table, PatientID is a primary key and not null.

51

4.2.1.6 Table questionnaire

Figure 4.6 : Table questionnaire

Table patient contain QuesID, Question, StageID, Answer1, Answer2, Mark1 and Mark2. In

this table, QuesID is a primary key, not null and StageID is a foreign key .

52

4.2.1.7 Table result

Figure 4.7 : Table result

Table result contain ResultID, PatientID, QuesID, Mark , Confirmation and DoctorID. In this

table, ResultID is a primary key, not null and PatientID, QuesID and DoctorID is a foreign

key .

4.2.1.8 Table stage

Figure 4.8 : Table stage

Table Stage contain StageID, and StageName. In this table, StageID is a primary key, not nul

53

4.2.2 Interface Design

4.2.2.1 User Module

Figure 4.9 : Homepage

Figure 4.9 show interface for User homepage. After login, User will enter this page. There

are menu in this page such as Home, About Alzheimers, Manage Profile, Questionnaire, and

Report Status.

54

Figure 4.10 : About Alzheimer’s

Figure 4.10 show interface for About Alzheimer. User can see all the information about the

Alzheimer’s disease.

Figure 4.11 : Manage Profile

Figure 4.11 show interface for User Manage Profile. This page consist of two function which

is user can update profile and also they can change password..

55

Figure 4.12 : Questionnaire

Figure 4.12 show interface for User Questionnaire. There will be a list of questionnaire about

Alzheimers’s disease detection and user need to answer all the question in order to get the

final result.

Figure 4.13 : Report Status

Figure 4.13 show interface for User Report Status. After answering questionnaire, User can

view their report status here. There will be two option here which is viewing the result or get

the confirmation from doctor. If user want to get the confirmation from the doctor ,they need

to wait for the doctor confirmation in few days and can view in this page too.

56

4.2.2.2 Nurse Module

Figure 4.14 : Homepage

Figure 4.14 show interface for Nurse homepage. After login, Nurse will enter this page.

There are menu in this page such as Home, Manage Profile, Manage Questionnaire, and

Report Questionnaire.

Figure 4.15 : Manage profile

Figure 4.15 show interface for Nurse Manage Profile. This page consist of two function

which is Nurse can update profile and also they can change password.

57

Figure 4.16 : Manage Questionnaire

Figure 4.16 show interface for Nurse Manage Questionnaire. This page consist of three

function which is Nurse can add Questionnaire, update Questionnaire and also they can delete

Questionnaire.

Figure 4.17 : Report Questionnaire

Figure 4.17 show interface for Nurse Questionnaire Report. Nurse can view the questionnaire

detail in this page.

58

4.2.2.3 Doctor Module

Figure 4.18 : Homepage

Figure 4.18 show interface for Doctor homepage. After login, Doctor will enter this page.

There are menu in this page such as Home, Manage Profile, Manage and Patient Report

Status.

Figure 4.19 : Manage Profile

Figure 4.19 show interface for Doctor Manage Profile. This page consist of two function

which is Doctor can update profile and also they can change password.

59

Figure 4.20 : Patient Result Status

Figure 4.20 show interface for Doctor Patient Report Status. Doctor can view patient report

status in order to give the confirmation.

60

4.3 SOLUTION COMPLEXCITY

4.3.1 Questionnaire Sample

Have you noticed any of these warning signs?

Note: Be aware that it may be difficult to place a person with Alzheimer’s in a specific stage

as stage may overlap.

1. Please state year of patient.

o 64 years below mark = 0

o 65 years abovemark = 1

Mild Alzheimer’s Disease (early - stage)

2. Problem coming up with the right word or name.

o Yes mark = 1

o No mark = 0

3. Trouble remembering names when introduced to new people.

o Yes mark = 1

o No mark = 0

4. Challenges performing task in social or work setting.

o Yes mark = 1

o No mark = 0

5. Forgetting material that one has just read.

o Yes mark = 1

o No mark = 0

6. Losing or misplacing a valuable object.

o Yes mark = 1

o No mark = 0

61

7. Increasing trouble with planning or organizing

o Yes mark = 1

o No mark = 0

Moderate Alzheimer’s disease ( middle-stage)

8. Forgetfulness of events or about one’s own personal history

o Yes mark = 1

o No mark = 0

9. Feeling moody or withdrawn, especially in socially or mentally challenging situation.

o Yes mark = 1

o No mark = 0

10. Being unable to recall their own address or telephone number or the high school or

college from which they graduated.

o Yes mark = 1

o No mark = 0

11. Confusion about where they are or what day it is.

o Yes mark = 1

o No mark = 0

12. The need for help choosing proper clothing for the season or the occasion.

o Yes mark = 1

o No mark = 0

13. Trouble controlling bladder and bowels in some individuals.

o Yes mark = 1

o No mark = 0

14. Changes in sleep patterns, such as sleeping during the day and becoming restless at

night.

o Yes mark = 1

o No mark = 0

62

15. An increasing risk of wandering and becoming lost.

o Yes mark = 1

o No mark = 0

16. Personality and behavioural changes, including suspiciousness and delusions or

compulsive, repetitive behaviour like hand- wringing or tissues shredding.

o Yes mark = 1

o No mark = 0

Severe Alzheimer’s disease(late-stage)

17. Need round the clock assistance with daily activities and personal care

o Yes mark = 1

o No mark = 0

18. Lose awareness of recent experiences as well as of their surroundings,

o Yes mark = 1

o No mark = 0

19. Experience changes in physical abilities, including the ability to walk, sit and,

eventually swallow.

o Yes mark = 1

o No mark = 0

20. Have increasing difficulty communicating.

o Yes mark = 1

o No mark = 0

21. Become vulnerable to infections , especially pneumonia.

o Yes mark = 1

o No mark = 0

63

score stage

0-7 Mild Alzheimer’s Disease (early - stage)

8-16 Moderate Alzheimer’s disease ( middle-stage)

17-21 Severe Alzheimer’s disease (late-stage)

$score = 0;

$score = $ans1 + $ans2 + $ans3 + $ans3 + $ans4 + $ans5 + $ans6 + $ans7 + $ans8 + $ans9 +

$ans10 + $ans11 + $ans12 + $ans13 + $ans14 + $ans15 + $ans16 + $ans17 +$ans18 +$ans19

+$ans20 +$ans21;

If ( ($score =17) ) {

$stage = Severe Alzheimer’s;

}

Elseif (($score =8) ) {

$stage = Moderate Alzheimer’s;

}

Elseif (($score =1) ) {

$stage = Mild Alzheimer’s;

}

64

65

CHAPTER V

CONCLUSION

5.1 INTRODUCTION

This chapter focused on project contribution, constraints of the project and its

conclusion, future works that can be gained from this project. From this project, we can find

out how to improve the system.

5.2 PROJECT CONTRIBUTION

Early Alzheimer’s Disease Detection System was successfully developed before its

timeline. This system very useful to another people. The people can aware about their

potential in getting Alzheimer’s Disease. Not only the patient that should aware, the family

members also need to be aware. This system help user to predict which stage of Alzheimer’s

Disease they suffering. This system also give the confirmation from the doctor to those who

need it.

5.2 PROJECT CONSTRAINTS AND LIMITATION

Every system must be have its own obstacle or difficulties in developing the system.

It can occur on developing phase or design phase. This constraints can effect the schedule for

developing system.

For this system, the difficulties that has been faced is its to hard to find the questions

that suitable for the user in Malaysia in predicting the Alzheimer’s Disease. Although already

found the suitable question, the questions still cannot replace laboratory test from hospital.

This system also cannot generate accurate result as the doctors do.

66

5.3 FUTURE WORKS

In the future, there are still a lot work can be made into this system. Firstly, for the

risk prediction, the paginations should be done to make friendly viewed by user. Next, more

information should be added in the system. The system should be have its security to improve

its user privileges.

5.4 CONCLUSION

As conclusion, this system has been implemented by using Decision Tree technique.

With Decision Tree technique, the stage of Alzheimer’s Disease can be identified. The user

can use this system because this system already achieved its objectives. However, the user

still need to go through details examination in hospital accurate result. This system are built

to aware the user about their potential to have Alzheimer’s Disease. An early step can

preserved the disease become worse. Be aware before late.

67

5.0 REFERENCES

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2. Ms. Leena Patil, Ms. Aparna Sirsat, Ms. Diksha Kamble, Mr.Yogesh Pawar,

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3. Sandhya Peddabachigari, Ajith Abraham*, Johnson Thomas Department of Computer

Science, Oklahoma State University, USA ―Intrusion Detection Systems Using

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4. Alzheimer’s Disease and Dementia |alz.org|Alzheimer’s Association.‖what is

Alzheimer’s.‖ .

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7. OR-Notes.‖Decision trees examples‖ J E Beasley

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Approach‖. IEEE Xplore Digital Library, 26 January

2015..

68

9. ―PHP Decision Tree Classifier: Compose decision trees and evaluate subjects‖.PHP

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10. Andrew Powell-Morse ―Iterative Model: What Is It And When Should You Use

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