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

Q: What is the clinical evidence from this study?

Study design: Other. Population: Coronary artery disease. Intervention: Stacking ensemble machine learning model with SMOTE vs. Other machine learning models. Primary outcome: Model performance (accuracy, precision, recall, AUC) (AUC 96.1%).

Key Result

A stacking ensemble machine learning model combined with SMOTE achieved high performance for long-term coronary artery disease risk prediction, demonstrating an accuracy of 90.9% and an AUC of 96.1%.

Structured PICO

Intervention

Stacking ensemble machine learning model with synthetic minority oversampling technique (SMOTE)

Comparator

Other machine learning models with or without SMOTE

Outcome

Model performance metrics (accuracy, precision, recall, AUC) for long-term coronary artery disease risk prediction

A stacking ensemble machine learning model utilizing SMOTE demonstrated high accuracy and AUC for the long-term prediction of coronary artery disease risk.

Main Result

Effect estimate: AUC 96.1%

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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Cite This Study

Τρίγκα et al. (Fri,) conducted a other in Coronary artery disease. Stacking ensemble machine learning model with SMOTE vs. Other machine learning models was evaluated on Model performance (accuracy, precision, recall, AUC) (AUC 96.1%). A stacking ensemble machine learning model combined with SMOTE achieved high performance for long-term coronary artery disease risk prediction, demonstrating an accuracy of 90.9% and an AUC of 96.1%.

synapsesocial.com/papers/6a1c5faec97d63156a5f9b21 https://doi.org/https://doi.org/10.3390/s23031193

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