Enhancing Heart Disease Model Accuracy: A Study of Optimization Hyperparameter Tuning Techniques in Machine Learning Algorithms

Heart disease (HD) is a major global health issue, This study evaluates Machine learning (ML) models such as Logistic Regression (LR), Decision Tree (DT), K- Nearest Neighbors (KNN), and Support Vector Machine (SVM) for heart disease dataset using Train-Test Split(TTS) and K-Fold Cross-Validation (KF-CV). Initially, accuracy rates improved with KF-CV across all models. The DT model achieved 98.54 percent accuracy with TTS and 99.32 percent with KF-CV in the pre-optimization phase. To further enhance accuracy, four advanced optimization techniques—Grid Search CV, Random Search CV, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) were applied. The findings revealed that the DT model achieved perfect score of 100 percent accuracy with Grid Search CV and Random Search CV. SVM and KNN models also showed significant improvements with advanced tuning. PSO, in particular, was effective for optimizing hyperparameters of the LR model. This study underscores the value of advanced optimization techniques in improving the accuracy of heart disease prediction models.