An Efficient Hybrid Classification Model for Heart Disease Prediction

Abstract

Heart disease prediction is a critical task in healthcare, aiming to identify individuals at risk and enable timely intervention. In this study, we propose a novel approach that combines a genetic algorithm for feature selection with a hybrid SVM-CNN model (GA-SVM-CNN) for heart disease prediction. The approach is evaluated on three di verse datasets: UCI, Z-Alizadeh Sani, and Cardiovascular Disease Dataset. First, the genetic algorithm is employed to select the most informative features from the datasets, reducing dimensionality and eliminating irrelevant or redundant features, and selecting the most appropriate features. Next, the hybrid SVM-CNN model is trained using the selected features, leveraging the strengths of both techniques for accurate prediction. The performance of the GA-SVM-CNN approach is evaluated using three benchmark datasets. On the UCI dataset, the approach achieves an impressive accuracy of 98%, indicating its effectiveness in accurately predicting heart disease. On the Z-Alizadeh Sani dataset, the approach achieves an accuracy of 97%. On the Cardiovascular disease Dataset, the approach achieves an accuracy of 86%. The high accuracy achieved by the GA-SVM-CNN approach demonstrates its efficacy in heart disease prediction across different datasets. The combination of the genetic algorithm’s feature selection and the hybrid SVM-CNN model’s predictive power contributes to superior performance. These results underscore the potential of this approach in supporting personalized healthcare solutions and improving patient outcomes.