Effectiveness of Neural Networks in Diagnosing Acute Inflammations of

Urinary System

Research report submitted for partial fulfilment of Artificial Intelligence

Coursework

Submitted by

Muhammad Umer Hasan & Sameeduddin Qureshi

In Department of Computer Science

University of Karachi

Abstract

Artificial Neural Networks are used to solve a wide range of problems. This paper proposes an

expert system which, by using artificial neural networks, learns to recognize the patterns of

diseases and accurately makes diagnosis. The proposed system uses Neural Networks to learn the

patterns of both diseases and shows how accurate it could be in identifying diseases. The neural

networks were trained with data taken from ‘Acute Inflammations Dataset’ from UCI machine

learning repository. This data set is about symptoms and diagnosis of two diseases of urinary

system which are inflammation of urinary bladder and Nephritis of renal pelvis origin. We found

that the system was 100% accurate in diagnosing inflammation of urinary bladder and 80%

accurate in diagnosing nephritis of pelvic origin after training.

Introduction

Diagnosis is one of the most risky decisions a doctor has to take. And if the decision is wrong it

could be claimed and affects the reputation of the doctor. One wrong diagnosis can result in wrong

treatment of a patient which can have severe consequences, sometimes fatal. And the same

decision, if claimed, could result in a doctor’s ‘professional death sentence’. When reviewing 25

years U.S. medical malpractice claims the researchers of John Hopkins Medicine found that only

diagnostic errors accounted for the largest fraction of claims. Diagnosis related payments

amounted to $38.8 billion in 1986-2010. The number of patients found to be diagnosed wrong in

U.S. ranges from 80,000 to 160,000 injuries or deaths annually [1]. Another study says wrong

diagnosis results in 40,000 to 80,000 deaths in United States annually [2].

Doctors, after all, are humans and are liable to make errors. So does a computer. But a computer

is not drawn towards a particular idea and is totally unbiased.

In this paper we propose an expert system that uses Neural Networks that learns to diagnose two

of the most difficult to diagnose diseases, viz. Inflammation of Urinary Bladder and Nephritis of

pelvis origin.

Various authors have suggested using Artificial Neural Networks in diagnosing different diseases.

Das, Turkoglu and Sengur (2009) examined effectiveness of Artificial Neural Networks in

diagnosis of heart disease [3]. They found 89.01% classification accuracy using ensemble based

methodology. Amato, et al (2013) proposed Artificial Neural Networks based models for diagnosis

of various diseases and found that they were reliable at processing large data and did not overlook

relevant information and reduction in diagnosis time [4]. Al-Shayea (2011) proposed the use of

Artificial Neural Network in diagnosing acute nephritis disease and heart disease and found feed-

forward back propagation network was 99% accurate in the classification of nephritis and 95%

accurate in classification of heart disease [5]. Atkov (2012) studied different types of neural

networks and their accuracy in predicting coronary heart disease. They found the best accuracy

with two hidden layer model topology included by both genetic and non-genetic CHD risk factors [6]. Ganesan (2010) studied the application of neural networks to ease the work of clinician and

increase cost effectiveness. They found that neural networks can effectively diagnose lung cancer

by using demographic data [7].

After reviewing existing body of research present study was designed to investigate whether neural

networks could be used for diagnosing acute inflammations of urinary system or not. It is suggested

findings of this research helps in accurately diagnosing these diseases and reduce the chances of

error in diagnosis. The proposed system could be used by doctors and health professionals for

quick and accurate diagnosis.

In accordance with previous researches it is hypothesized that this system would be able to give

significantly accurate diagnosis of inflammation of urinary bladder and nephritis of pelvic origin.

Experimental Context

The data used for this research was taken from UCI’s machine learning repository [8]. The dataset

is named ‘Acute Inflammations’ and contains symptoms and diagnosis of two diseases, viz.

inflammation of urinary bladder and nephritis of pelvis origin. Table 1 shows the description of

dataset.

Table 1

Variables Attribute Names Representation in Dataset

X1 Temperature of Patient Numerical data, Ranges from 35-42°C

X2 Occurrence of Nausea Yes or No

X3 Lumbar Pain Yes or No

X4 Urine pushing (continuous

need for urination)

Yes or No

X5 Micturition Pains Yes or No

X6 Burning of Urethra, itching,

swelling, etc.

Yes or No

X7 Decision: Inflammation of

Urinary Bladder

Yes or No

X8 Decision: Nephritis of pelvic

origin

Yes or No

Methodology

Artificial Neural Networks

Artificial Neural Networks are information processing systems. They start from blank and learn

by adjusting weights after comparing their activation value with the real output. These networks

are composed of neurons. Each neuron can do a single task, and collectively they are called neural

network. Working of a single neuron is shown in figure 1 [9].

Figure 1

Inputs

They are the values which are given provided to the neural network to work upon. The inputs are

encoded so that they can be represented become useful for the network and out in the activation

function [10]. Table 2 shows the encoding of data used in this research.

A yes/no value can be encoded in 0 and 1, where 0 represents no and 1 represents yes.

A numerical can be divided or multiplied to help the activation function generate values

near to the threshold.

For a categorical data, such as religion or political interest, we have to add input neurons

one for every category.

Table 2

Input Values Encoded Value

1. Temperature 35.0 - 45.5 Divided by 100, so the input

is between 0.35 to 0.45

2. Occurrence of Nausea Yes or No 1 or 0

3. Lumbar Pain Yes or No 1 or 0

4. Urine pushing (continuous

need for urination)

Yes or No 1 or 0

5. Micturition Pains Yes or No 1 or 0

6. Burning of Urethra,

itching, swelling, etc.

Yes or No 1 or 0

7. Decision: Inflammation of

Urinary Bladder

Yes or No 1 or 0

8. Decision: Nephritis of

pelvic origin

Yes or No 1 or 0

Weights and Activation function

Every input is assigned a weight and the sum of the products of all the inputs with their weights is

called Activation function. Afterwards the value generated by activation function is checked

against a ‘Threshold value’. If the value exceeds the threshold value, the neuron is activated and

transfers the output to next layer. If not, the neuron is not considered activated.

𝑌 =∑ 𝑊𝑖 ∗ 𝑋𝑖𝑛

𝑖=1

Activation Function [11]

where,

wi = weight of the ith input

xi = ith Input

Backtracking

The real idea behind training neural networks is machine learning, i.e., learning from mistakes.

The neurons check their activation with respect to the output provided in training. If their output

is wrong they use a backtracking function to adjust their weights. This adjustment of weights is

called leaning or backtracking and its formula is given below.

Wi = Wi + (a * Xi * e) [12]

where,

Wi = weight of ith input

a = learning rate (in this research it was kept 0.2)

Xi = ith input

e = error (error = actual output value – calculated output)

Network Model

Figure 2

Sample Pseudo code for Neurons

Step 1. Set Y = 0, a = 0.2;

Step 2. Get weights from database

Step 3. Get the first instance of inputs from dataset

Step 4. for I = 1 to 6

Y = Y + Wi*Xi

Step 5. If Y > 0, Y = 1

Else Y = 0

Step 6. Error = Actual Value of Diagnosis (0 or 1) – Y

Step 7. For I = 1 to 6

Wi = Wi + (a * Xi * Error)

Step 8. Update the weights in database

Step 9. Repeat steps 1 through 8 until there is no error in a complete iteration over complete

dataset.

{For the complete code of the software system, see Appendix A}

Result

Neuron 1 and Neuron 2 were trained 4 times to finally diagnose inflammation of urinary bladder

and nephritis of pelvic origin. The initial value for all weights was set to 0.5. After training the

weights adjusted through backtracking. Graph 1 and Graph 2 show the final weights, after training,

used by the artificial neural network to diagnose both diseases. Graph 3 shows the number of errors

in each epoch (iteration) which, as we can see, there are none after training 4 times. These are the

final values and they don’t need to be changed for the training dataset.

Graph 1 – shows values of weights to successfully diagnose inflammation of urinary bladder

Graph 2 – shows values of weights to successfully diagnose nephritis of pelvic origin

Temperature

NauseaLumbar

PainUrine

PushingMicturition

Pains

Burning,itching,

swelling ofUrethra

Series1 -0.102 0.3 -0.9 0.5 0.3 0.1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Final weights to diagnose inflammation of urinary bladder

Temperature

NauseaLumbar

PainUrine

PushingMicturition

Pains

Burning,itching,

swelling ofUrethra

Series1 -0.1578 0.5 -0.1 0.1 0.3 0.1

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Final weights to diagnose nephritis of pelvis origin

Graph 3 – shows the number of errors in each iterations (or number of weight adjustment),

which finally stops at the 4th iteration.

Finally the neural network was tested for new data which was especially kept for testing purposes

and was not included in training dataset. It revealed that the neural network can accurately diagnose

inflammation of urinary bladder to 100% and nephritis of pelvic origin to 80%. Furthermore it can

be seen that statistically both the percentage of correct diagnoses are significant.

Graph 4 – shows the accuracy (in percentage) of neural network for diagnosis

0

2

4

6

8

10

12

1 2 3 4

Errors in each iteration

N1 N2

0

20

40

60

80

100

120

Inflammation of urinary bladder Nephritis of pelvic origin

Testing - Accuracy of Neural Network in Diagnosis

Conclusion

The main objective of this paper was to validate the use of neural networks for diagnosing

inflammation of urinary bladder and nephritis of pelvic origin. This could be achieved with a

number of algorithms but use of neural networks was proposed because neural networks are

flexible and can perform a number of operations which cannot be achieved through linear

programming. They are fault tolerant and adaptive. They use learning algorithms so the more a

neural network is used, the more accurate it becomes.

The neural networks were trained until the weights were completely adjusted with respect to the

training data. Finally, new data was used for testing purpose and it revealed that the proposed

system diagnosed inflammation of urinary bladder 100% accurately and diagnosed nephritis of

pelvic origin with 80% accuracy.

The accuracy could be enhanced and will be increased with more training data. The training dataset

of ‘Acute Inflammations’ downloaded from UCI’s machine learning repository only consisted of

120 instances from which some repeated data was eliminated and testing data was also excluded.

So it amounted 70 instances for dataset and 20 instances for testing. If the more training data is

provided or the system is used in a hospital for some time, its accuracy will become more

significant. However, from results it can be concluded that neural networks can be used to

efficiently diagnose inflammation of urinary bladder and nephritis of pelvic origin.

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8. https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations

9. http://ulcar.uml.edu/~iag/CS/A-Neuron.gif

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