Diabetes Prediction using Support Vector Machine, Naive Bayes and Random Forest Machine Learning Models

Abstract

Nowadays, diabetes is a relatively prevalent condition. This illness affects a large number of people worldwide. Numerous renal and cardiac disorders are mostly caused by diabetes. The major factor causing high blood glucose levels is diabetes. In this study, Machine Learning (ML) algorithms are utilized to estimate the likelihood that a person would get diabetes. The primary foundation of the machine learning model is a set of data. These are statistical algorithms that be taught or trained using data with hidden patterns. This study predicts diabetes using three ML models. The experimental findings demonstrate that the Random Forest ML algorithm predicts diabetes with an accuracy of 88.14 percent.