Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

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%.

Keywords: coronary artery disease; feature analysis; healthcare; long-term risk prediction; machine learning.

Figure 1

Gain ratio features’ importance evaluation before and after SMOTE.

Figure 2

RF features’ importance evaluation before and after SMOTE.

Figure 3

Performance Evaluation with AUC ROC Curves before SMOTE.

Figure 4

Performance Evaluation with AUC ROC Curves after SMOTE.