Machine learning and balanced techniques for diabetes prediction

Diabetes mellitus is a metabolic disorder characterized by high blood glucose levels, resultingfrom defects in insulin secretion, insulin action, or both. This study applied some supervised learning such Support Vector Machine, Random Forest and Gradient Boosting to predict diabetes mellitus. Additionally, a comparative analysis of two balanced data techniques, namely SMOTE and Random UnderSampler, is presented. Results show that Gradient Boosting yielded the most favorable outcomes in terms of accuracy and precision when utilizing SIIOTE technique. Furthermore, the inclusion of insulin variable and the exclusion of Skinlhickwess and BloodPressure variables led to improve the results.