Performance Assessment of a Clinical Support System for Heart Disease Prediction Using Machine Learning

Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve clinical outcomes.Traditional diagnostic approaches relying on conventional clinical data analysis often encounter limitations in precision and efficiency.Machine learning (ML) techniques offer a promising solution by enhancing predictive accuracy and decision-making capabilities.This study evaluates the performance of a clinical support system (CSS) for heart disease prediction using a hybrid classification approach that integrates support vector machine (SVM) and k-nearest neighbor (KNN).Patient data were stratified by age group and gender to assess the model's performance across diverse demographic profiles.Key performance metrics, including accuracy, recall, precision, F1-score, and area under the curve (AUC), were employed to quantify predictive efficacy.Experimental results demonstrated that the combined SVM-KNN model achieved superior classification performance, yielding an accuracy of 97.2%, recall of 97.6%, precision of 96.8%, AUC of 97.1%, and an F1-score of 98.2%.These findings indicate that the integration of SVM and KNN enhances heart disease prediction accuracy, thereby reinforcing the potential of CSS in improving early diagnosis and patient management.