Key result
A SMOTE-enhanced stacking ensemble model predicts CAD with ~91% accuracy.
Why the study?
Coronary artery disease can be delayed or prevented with lifestyle changes and medical intervention, making long-term risk prediction of CAD an area of clinical interest.
Does a stacking ensemble machine learning model with SMOTE improve the prediction of long-term coronary artery disease risk?
Does a stacking ensemble machine learning model with SMOTE improve the prediction of long-term coronary artery disease risk?
A stacking ensemble machine learning model combined with SMOTE data balancing achieved high accuracy and AUC for predicting long-term coronary artery disease risk.
Should not yet alter CAD risk prediction in practice; leaves open whether stacking ensembles with SMOTE improve outcomes in prospective studies.
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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Τρίγκα et al. (2023) studied Coronary artery disease (n=3,655). Stacking ensemble machine learning model with SMOTE vs. Other machine learning models and models without SMOTE was evaluated on Prediction accuracy and AUC for coronary artery disease. A stacking ensemble machine learning model using the synthetic minority oversampling technique (SMOTE) predicted coronary artery disease with 90.9% accuracy and an AUC of 96.1%.
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