<![CDATA[Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study aims to enhance classification accuracy by applying a hybrid PSOGWO (Particle Swarm and Grey Wolf Optimization) method for feature selection and a standard scaler for data balancing in SVM classification.Methods/Study design/approach: The research begins with obtaining a heart disease dataset, which undergoes preprocessing steps, including feature selection using hybrid PSOGWO and data normalization with a standard scaler. The dataset is then divided into training and testing sets, where the training data is classified using SVM. Performance evaluation is conducted using a confusion matrix to measure accuracy improvements. Result/Findings: The proposed method successfully selects 10 significant features out of 13 in the dataset. By integrating hybrid PSOGWO with SVM, the classification accuracy improves to 93.66%, representing a 2.44% increase from the original 91.22% obtained using SVM without feature selection. Novelty/Originality/Value: This research contributes to the development of more effective heart disease prediction models by optimizing feature selection and classification, leading to improved diagnostic accuracy and potential clinical applications.]]>
