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Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features
Roohallah Alizadehsani1, Jafar Habibi1, Zahra Alizadeh Sani2
1Department of Computer Engineering, Sharif University of Technology, Tehran, IR Iran.
Insights
This study used data mining to predict coronary artery disease (CAD) stenosis, identifying high-risk patients and reducing unnecessary angiography. The models achieved high accuracy, particularly for the left anterior descending artery, offering a cost-effective alternative.
Area of Science:
- Cardiovascular medicine
- Medical data analysis
- Biostatistics
Background:
- Coronary artery disease (CAD) is a leading cause of global mortality, stemming from plaque buildup in coronary arteries.
- Angiography is the standard for diagnosing artery stenosis but has significant drawbacks.
- Developing non-invasive, cost-effective diagnostic methods for CAD is crucial.
Purpose of the Study:
- To predict arterial stenosis using data analysis and data mining algorithms.
- To identify patients with a high probability of normal coronary arteries, thereby avoiding unnecessary angiography.
- To select high-risk patients who require angiography for definitive diagnosis.
Main Methods:
- Utilized laboratory and echocardiographic data from 303 patients to predict stenosis in individual coronary arteries.
- Employed Bagging and C4.5 classification algorithms for data analysis.
- Identified key features and established rules for evaluating factors like triglyceride levels, hemoglobin, hypertension, dyslipidemia, diabetes mellitus, and ejection fraction.
Main Results:
- Bagging algorithm achieved diagnostic accuracy rates of 79.54% for LAD, 61.46% for LCX, and 68.96% for RCA stenosis.
- Feature selection was crucial for achieving high accuracy in predicting LAD stenosis.
- Extracted rules for evaluating specific patient features related to CAD risk.
Conclusions:
- The study demonstrates the potential of data mining techniques for non-invasive CAD risk assessment.
- Achieved the highest reported accuracy for diagnosing left anterior descending (LAD) artery stenosis in the literature.
- Highlights the utility of echocardiographic and laboratory data in predicting the need for angiography.
Background:
Coronary artery disease (CAD) is the result of the accumulation of athermanous plaques within the walls of coronary arteries, which supply the myocardium with oxygen and nutrients. CAD leads to heart attacks or strokes and is, thus, one of the most important causes of death worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing artery stenosis. However, the disadvantages of this method such as complications, costs, and possible side effects have prompted researchers to investigate alternative solutions.
Objectives:
The current study aimed to use data analysis, a non-invasive and less costly method, and various data mining algorithms to predict the stenosis of arteries. Among many people who refer to hospitals due to chest pain, a great number of them are normal and as such do not need angiography. The objective of this study was to predict patients who are most probably normal using features with the highest correlations with CAD with a view to obviate angiography costs and complications. Not a substitute for angiography, this method would select high-risk cases that definitely need angiography.
Patients And Methods:
Different features were measured and collected from potential patients in order to construct a dataset, which was later utilized for model extraction. Most of the proposed methods in the literature have not considered the stenosis of each artery separately, whereas the present study employed laboratory and echocardiographic data to diagnose the stenosis of each artery separately. The data were gathered from 303 random visitors to Rajaie Cardiovascular, Medical and Research Center. Electrocardiographic (ECG) data were studied in our previous works. The goal of this study was, therefore, to seek the accuracy of echocardiographic and laboratory features to predict CAD patients that require angiography.
Results:
Bagging and C4.5 classification algorithms were drawn upon to analyse the data, the former reaching accuracy rates of 79.54%, 61.46%, and 68.96% for the diagnosis of the stenoses of the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. The accuracy to predict the LAD stenosis was attained via feature selection. In the current study, features effective in the stenosis of arteries were further determined, and some rules for the evaluation of triglyceride, hemoglobin, hypertension, dyslipidemia, diabetes mellitus, and ejection fraction were extracted.
Conclusions:
The current study presents the highest accuracy value to diagnose the LAD stenosis in the literature.
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