Comparison of Support Vector Machine, Naive Bayes, and K-Nearest Neighbors Algorithms for Classifying Heart Disease

Heart disease has been the leading cause of death in the EU for many years. Early detection of this disease increases a patient's chance of survival. The aim of the study is to see if machine learning algorithms can help in the early diagnosis of these illnesses. For this purpose, three classifiers: kNN, Naive Bayes and SVM were implemented and trained on a dataset containing medical data related to the possibility of cardiovascular disease. The result of the study is a comparative analysis of the classifiers that summarises the accuracy and stability of the results in determining the possibility of heart disease. The results show the highest accuracy and stability of the SVM classifier, which achieves an average of 82.47% accuracy in disease prediction, meaning that machine learning algorithms can significantly aid in the early diagnosis of patients based on their basic medical data.