A Classification Approach for Effective Noninvasive Diagnosis of Coronary Artery Disease Advisor: 黃三益 教授 Student: 李建祥 D 楊宗憲 D 張珀銀 D

Outline Background Motivation The Data Mining Process Conclusion Limitation

Background Heart disease is the leading cause of death in Taiwan.  The third on the rank of the number of people died  The number of people died is 12,970  The death rate is 57.1% per one hundred thousand people Coronary artery disease (CAD) is the most common type of heart disease.

The illustration of CAD Source:

The Diagnosis of CAD Noninvasive approaches  Laboratory tests  Electrocardiogram (ECG)  Ultrasound tests Invasive approach  Coronary angiography

The Important Risk Factors of CAD Smoking High blood pressure High blood cholesterol Diabetes Being overweight or obese Physical inactivity

Motivation Invasive approach is higher risk and cost than noninvasive approach Is noninvasive approach is sufficient to diagnose the possibility of occurring CAD? What’s the performance of noninvasive approaches?

Motivation Invasive approach is higher risk and cost than noninvasive approach Is noninvasive approach is sufficient to diagnose the possibility of occurring CAD? What’s the performance of noninvasive approaches?

The Data Mining Procedure: Step 1 Use some medically examinations to predict whether some people have heart disease. A Classification problem

The Data Mining Procedure: Step 2 UCI KDD archive web Those row data come from three hospitals in United States

The Data Mining Procedure: Step 3 AttributesRange age Min ＝ 28 Max ＝ 77 Average ＝ 54 Sex Male=1 Female=0 Chest pain type1:Typical angina 2:Atypical angina 3:Non- angina pain 4:Asymptomatic resting blood pressure*[0,120] =0 [121, ∞) =0 cholestoral *0: >200 1: [200,240) 2: >240 fasting blood sugar0: <=120 1: >120 electrocardiographic0:Normal 1:Having ST-T wave abnormality 2:Left ventricular hypertrophy maximum heart rate[60,138] AttributesRange exercise induced angina Yes = 1 No = 0 ST depression induced by exercise The slope of peak exercise ST segment Upsloping = 1 flat= 2 Downsloping =3 resting blood pressure >120 =1 <120 =0 Number of major vessels [0,1,2,3] thalNormal = 3 Fixed defect = 6 Reversable defect = 7 diagonsis< 50% diameter narrowing = 0 > 50% diameter narrowing = 1

The Data Mining Procedure: Step 4 Source: UCI KDD Archive Training set: Cleveland Clinic Foundation, 303 records Testing set: Hungarian Institute of Cardiology, 294 records

The Data Mining Procedure: Step 5 The Problem of data  Missing Value Approach  Discard the records containing missing values

The Data Mining Procedure: Step 6 Skip this step due to the uselessness of aggregating records or combining original attributes

The Data Mining Procedure : Step 7 In order to obtain rules from models to support medical decision  Decision tree C4.5 and Bays Network are used as our data mining approaches  WEKA is used as our data mining tool

The Data Mining Procedure : Step 7 (Cont.) WEKA tools screenshot

The Data Mining Procedure : Step 8 In order to assess the models, we conduct two phrases experiments with comprehensive measures which include sensitivity, specificity and accuracy.

The Data Mining Procedure : Step 8 (Cont.) First Phrase  The diverse combinations of fields were used as input variables. The fields are divided into four groups

The Data Mining Procedure : Step 8 Model Assessment We use sensitivity, specificity and accuracy as our model measures. Sensitivity could represent the probability of mistake in diagnosis Specificity could represent the probability of unnecessary medical resource wasting

The Data Mining Procedure : Step 8 The Result of First Phase

The Data Mining Procedure : Step 8 The Result of First Phase (Cont.)

The Data Mining Procedure : Step 8 The Result of Second Phrase

The Data Mining Procedure : Step 9 In this step, due to we have not enough medical resource to support our project, it is difficult to deploy our models in practical. Although we cannot fulfill our models in the real business environment, we still obtain copious experience and knowledge throughout data mining process.

Conclusion Two classification approaches: decision tree and Bayesian network. Using noninvasive and invasive approaches step by step.

Limitation Limited noninvasive approaches  Apply other non-invasive examinations to increase performance of data mining model, such as ultrasound tests. Lack of explanation of rules with domain knowledge