A Novel Hybrid Approach for Feature Selection in Cardiovascular Risk Assessment

Early detection of cardiac risk is crucial for accurate diagnosis and treatment of fatal cardiovascular diseases. Selecting relevant features is essential for machine learning in building an effective decision support system of cardiovascular risk assessment, ensuring accuracy of high-dimensional data. This study aims to propose a novel hybrid feature selection approach, termed ant colony optimization with hill climbing (ACOHC), integrating ant colony optimization (ACO) and hill climbing (HC) algorithms. The accuracy metric and various classifiers are deployed to evaluate the effectiveness. Additionally, comparisons are made with nine alternative feature selection techniques. The feature subset identified through the ACOHC attains a classification accuracy of 95.1% with the support vector machine classifier.