Diagnosis of each main coronary artery stenosis based on whale optimization algorithm and stacking model
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Insights
This study introduces a computer-aided diagnosis model using a k-nearest neighbor-based whale optimization algorithm for feature selection and a stacking model for diagnosing coronary artery disease (CAD). The model achieved high accuracy in identifying narrowed left anterior descending, left circumflex, and right coronary arteries.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Cardiovascular disease (CVD) poses a significant global health burden, with coronary artery disease (CAD) being a primary contributor.
- CAD involves the narrowing of major coronary arteries: left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA).
- Accurate and early diagnosis of CAD is crucial for effective patient management and improved outcomes.
Purpose of the Study:
- To develop and evaluate a novel computer-aided diagnosis (CAD) model for the accurate detection and prediction of coronary artery disease.
- To enhance feature selection for CAD diagnosis using a k-nearest neighbor (KNN)-based whale optimization algorithm (WOA).
- To improve diagnostic performance by integrating selected features into a two-layer stacking model.
Main Methods:
- Utilized the whale optimization algorithm (WOA) with k-nearest neighbor (KNN) for optimal feature selection in coronary artery diagnosis.
- Implemented a binary conversion threshold within WOA to identify optimal feature subsets for LAD, LCX, and RCA.
- Developed a two-layer stacking model incorporating the selected features for diagnosing LAD, LCX, and RCA.
Main Results:
- The proposed KNN-based WOA method successfully selected 17 optimal features for each main coronary artery.
- Achieved high classification accuracies: 89.68% for LAD, 88.71% for LCX, and 85.81% for RCA on test sets.
- Demonstrated superior performance compared to other metaheuristic feature selection methods and machine learning algorithms on the Z-Alizadeh Sani dataset.
Conclusions:
- The KNN-based WOA effectively identifies optimal feature subsets for coronary artery disease diagnosis.
- The developed stacking model, utilizing these features, significantly improves the accuracy of CAD diagnosis for major coronary arteries.
- This computer-aided approach shows promise for enhancing the early detection and prediction of cardiovascular disease.
Abstract:
Cardiovascular disease is currently one of the diseases with high morbidity and mortality worldwide. One of the main types is coronary artery disease (CAD), which occurs when one or more of the three main arteries, the left anterior descending (LAD) artery, the left circumflex (LCX) artery, and the right coronary artery (RCA), are narrowed. In this paper, we introduce a computer-aided diagnosis model, which uses the k-nearest neighbor (KNN)-based whale optimization algorithm (WOA) for feature selection and combines stacking model for CAD diagnosis and prediction. In WOA, the values in the solution vectors are all continuous, and a threshold is set for binary-conversion to obtain the optimal feature subsets of each main coronary artery. Then we develop a two-layer stacking model based on the selected feature subsets to diagnosis LAD, LCX and RCA. By the proposed method, we select 17 features for each main artery diagnosis, and the classification accuracy on LAD, LCX, and RCA test sets is 89.68, 88.71 and 85.81%, respectively. On the Z-Alizadeh Sani dataset, we compare the proposed feature selection method with other metaheuristics and compare the performance of WOA based on different wrappers. The experimental results show that, the KNN-based WOA method selects the optimal feature subsets, and the classification performance of the stacking model is better than other machine learning algorithms.
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