Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model
Javad Hassannataj Joloudari1, Edris Hassannataj Joloudari2, Hamid Saadatfar1
1Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
Insights
This study enhances coronary artery disease (CAD) diagnosis accuracy using machine learning feature selection. Random Trees (RTs) model demonstrated superior performance over other methods for improved cardiovascular disease detection.
Area of Science:
- Cardiology and Medical Informatics
- Machine Learning in Healthcare
Background:
- Coronary artery disease (CAD) is a leading cause of mortality in middle-aged populations.
- Traditional diagnostic methods like angiography are expensive and carry risks.
- There is a need for more accurate and accessible CAD diagnostic tools.
Purpose of the Study:
- To improve the accuracy of coronary heart disease diagnosis.
- To identify and rank significant predictive features for CAD.
- To develop an integrated machine learning approach for enhanced diagnosis.
Main Methods:
- Utilized machine learning algorithms including Random Trees (RTs), C5.0 decision tree, Support Vector Machine (SVM), and Chi-squared automatic interaction detection (CHAID).
- Employed feature selection techniques to identify key diagnostic indicators.
- Integrated multiple machine learning models for comparative analysis.
Main Results:
- The proposed integrated machine learning method demonstrated promising diagnostic accuracy.
- The Random Trees (RTs) model significantly outperformed C5.0, SVM, and CHAID in diagnostic accuracy.
- Feature ranking identified crucial predictors for coronary artery disease.
Conclusions:
- Machine learning, particularly the RTs model, offers a powerful approach to enhance CAD diagnosis.
- Feature selection is critical for improving the accuracy of cardiovascular disease prediction.
- This study provides a foundation for developing more effective and cost-efficient CAD diagnostic strategies.
Abstract:
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
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