Performance comparison in optimization algorithms for heart disease detection model

Cardiovascular diseases are the leading cause of mortality worldwide, necessitating robust predictive models for early diagnosis. This study evaluates and compares the performance of support vector machine (SVM) models optimized using genetic algorithm (GA) and particle swarm optimization (PSO) techniques. A publicly available dataset with health-related features was used, involving preprocessing steps like handling missing values, encoding categorical variables, and feature scaling. The models were assessed based on cross-validation accuracy, test accuracy, and F1 score. Convergence plots were generated to visualize the optimization process. Results highlight the effectiveness of GA and PSO optimizations in enhancing SVM model performance for predicting cardiovascular health outcomes. The comparison provides insights into the relative strengths of each optimization method, guiding the development of advanced predictive models for heart diseases diagnosis and management.