1Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Knowledge discovery with classification rules in a cardiovascular dataset

Advisor : Dr. Hsu

Presenter : Zih-Hui Lin

Author :Viii Podgorelec a,*, Peter Kokol a, Milojka Molan Sti81ic b, Marjan Heri :ko a, Ivan Rozrnan a

Computer Methods and Programs in Biomedicine, Volume 80, Supplement 1, December 2005, Pages S39-S49

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Motivation Objective The AREX algorithm Experiment Conclusions

Outline

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Motivation

Modern medicine generates huge amounts of data and there is an acute and widening gap between data collection and data comprehension.

it is very difficult for a human to make use of such amount of information (i.e. hundreds of attributes, thousand of images, several channels of 24 hours of ECG or EEG signals)

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Objective

confirm their existing knowledge about medical problem (ie. hundreds of attributes)

enable searching for new facts, which should reveal some new interesting patterns and possibly improve the existing medical knowledge.

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The AREX algorithm

s

N

1.2Classify object with nt randomly chosen trees

S*

1.4From all N decision create M initial classification rules

1 multi-population self-adapting genetic algorithm for the induction of decision trees.

2 evolution of programs in an arbitrary programming language, which is used to evolve classification

3 an optimal set of classification rules is determined with a simple genetic algorithm

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1.3if frequency of the most frequent decision class classified by nt trees > nt - ct

Oi

(ct=nt/2)

2.1create m/2+1 rules (randomly)

2.3 If s is not empty•Add |s| randomly chosen objects from s* to s•ct=ct+1•repeat 1.1

1.1Build N decision trees upon objects from S

2.2

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2. Select an attribute Xi

Genetic algorithm for the construction of decision trees

population

1.Number of attribute nodes M that will be in the tree

M attributes

Xi

null null

root

null nullXi

null null

4. Randomly select an attribute Xi( 還沒被選過的機率較高 )

•For each empty leaf the following algorithmdetermines the appropriate decision class

3. 選一空節點， (tree 深度愈高，選中機率愈低 )

Xi

null null

(1)Continuous attributes→split constant(2)Discrete attributes →randomly defined two disjunctive sets

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I. M.proGenesys system & Finding the optimal set of rules

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Introduction

Advantage ─ transparency of the classification process that one can

easily interpret, understand and criticize.

Disadvantages─ poor processing of incomplete, noisy data,─ inability to build several trees for the same dataset─ inability to use the preferred attributes, etc.

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Dataset contains data of 100 patients from Maribor Hospital. The attributes include

─ general data (age, sex, etc.)─ health status (data from family history and child's previous illnesses), ─ general cardiovascular data (blood pressure, pulse, chest pain, etc.) ─ more specialized cardiovascular data - data from child's cardiac history

and clinical examinations (with findings of ultrasound, ECG, etc.). dataset five different diagnoses are possible:

─ innocent heart murmur良性雜音─ congenital heart disease with left-to-right shunt先天性心臟病 (左向右分流 )

─ aortic valve disease with aorta coarctation,主動脈辨疾病 (主動脈縮窄 )

─ arrhythmias心律不整─ chest pain.心悸

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Classification result –training set

Overfitting

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Classification result –testing set

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I. M.Classification result

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I. M.Conclusions

One of the most evident advantages of AREX is the simultaneous very good ─ Generalization → high and similar overall accuracy on both

training set and test set─ Specialization → high and very similar accuracy of all decision

classes, also the least frequent ones.

equip physicians with a powerful technique to─ (1) confirm their existing knowledge about some medical

problem─ (2) enable searching for new facts, which should reveal some new

interesting patterns and possibly improve the existing medical knowledge.

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I. M.My opinion

Advantage: 依屬性給予權重 Disadvantage: Apply: 台中醫院？