Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models
1Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.
Insights
This study enhances coronary artery disease (CAD) risk prediction using machine learning. A stacking ensemble model with synthetic minority oversampling technique (SMOTE) achieved 90.9% accuracy, outperforming other methods for long-term CAD risk assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary artery disease (CAD) arises from atherosclerotic plaques narrowing heart arteries, obstructing blood flow.
- Early detection and prevention are crucial for managing this life-threatening condition.
- Accurate long-term risk prediction is essential for timely intervention and patient management.
Purpose of the Study:
- To evaluate and compare various machine learning (ML) models for long-term coronary artery disease (CAD) risk prediction.
- To assess the impact of the synthetic minority oversampling technique (SMOTE) on model performance.
- To identify the optimal ML model for predicting CAD risk.
Main Methods:
- Experimentation with multiple machine learning (ML) models.
- Application and evaluation of the synthetic minority oversampling technique (SMOTE).
- Utilizing 10-fold cross-validation to assess model accuracy, precision, recall, and Area Under the Curve (AUC).
Main Results:
- The stacking ensemble model, combined with SMOTE and 10-fold cross-validation, demonstrated superior performance.
- This model achieved an accuracy of 90.9%, precision of 96.7%, recall of 87.6%, and an AUC of 96.1%.
- The results indicate the effectiveness of SMOTE in improving ML model performance for CAD risk prediction.
Conclusions:
- Machine learning models, particularly the stacking ensemble with SMOTE, show significant promise for accurate long-term coronary artery disease risk prediction.
- The findings suggest that SMOTE can enhance the predictive power of ML models in this domain.
- This approach offers a valuable tool for improving cardiovascular health outcomes through early risk identification.
Abstract:
The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%.
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