A Hybrid SVM and PSO Approach in The Classification of Hypertension at Medika Palopo General Hospital

Abstract

Hypertension is a chronic disease that often goes undetected in its early stages, increasing the risk of complications such as stroke and heart failure. Accurate classification of hypertensive patients is essential to support early intervention and reduce morbidity and mortality. This study aims to evaluate the performance of the Support Vector Machine (SVM) algorithm and to develop a hybrid classification model by integrating Particle Swarm Optimization (PSO) to improve the predictive performance of SVM. The research was conducted using 400 patient records from Medika Palopo General Hospital, equally divided into hypertensive and non-hypertensive groups, with 12 clinical features as input variables. The SVM model was built using a sigmoid kernel with default parameters (C = 1.0, gamma = auto), while the hybrid model utilized PSO to optimize the values of C and gamma. Evaluation results show that the conventional SVM model achieved an accuracy of 61.25%, precision of 63.22%, recall of 56.50%, F1-score of 59.31%, and AUC of 0.6400. After optimization using PSO, the hybrid model significantly improved with an accuracy of 96.75%, precision of 97.14%, recall of 96.31%, F1-score of 96.73%, and AUC of 0.9725. The findings indicate that the hybrid SVM-PSO approach effectively enhances the classification performance of hypertension prediction models and offers promising potential to be developed into an AI-based medical decision support system.