Comparison of SVM, KNN, And Naïve Bayes Algorithms in Monkeypox Disease Classification

Abstract

Advances in medical technology have enabled the application of machine learning for disease classification, including monkeypox. Monkeypox is a zoonotic disease caused by the monkeypox virus and can be detected through patient data. This study aims to compare the performance of Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Naïve Bayes algorithms in building a monkeypox classification model. The dataset used consists of 25,000 patient records. The results show that the SVM model with a linear kernel achieved the best accuracy compared to KNN and Naïve Bayes. These findings demonstrate that the SVM model with a linear kernel is highly effective in classifying monkeypox, offering great potential for further medical applications.