Machine Learning Based Prediction of Diabetes Using Support Vector Machines

Abstract

Diabetes is a highly prevalent and dangerous disease worldwide, leading to various complications such as heart failure, vision loss, and kidney diseases. Patients are often required to visit diagnostic centers to receive their consultation reports. Early prediction of the disease can be crucial in providing timely interventions to patients. Data mining techniques enable the extraction of hidden information from extensive datasets related to diabetes. This research aims to develop a system that can accurately predict the risk level of diabetes in patients. The proposed model utilizes Support Vector Machine (SVM) algorithms for prediction, achieving an accuracy of 87.3%. The results demonstrate the effectiveness and accuracy of the employed methods.