International Research Journal of Education and Technology

The wide adaptation of computer-based technology in the health care industry resulted in the accumulation of electronic data. Due to the substantial amounts of data, medical doctors are facing challenges to analyze symptoms accurately and identify diseases at an early stage. However, supervised machine learning (ML) algorithms have showcased significant potential in surpassing standard systems for disease diagnosis and aiding medical experts in the early detection of high-risk diseases. In this literature, the aim is to recognize trends across various types of supervised ML models in disease detection through the examination of performance metrics. The most prominently discussed supervised ML algorithms were Naive Bayes (NB),Decision Trees (DT), K-Nearest Neighbor (KNN). As per findings, Support Vector Machine (SVM) is the most adequate at detecting kidney diseases and Parkinson’s disease. The Logistic Regression(LR) performed highly at the prediction of heart diseases. Finally , Random Forest (RF), and Convolutional Neural Networks (CNN)predicted in precision breast diseases and common diseases , respectively.