Random forest and genetic algorithm united with hyperparameter for diabetes prediction by using WBSMOTE, wrapper approach

Abstract

Food is converted into energy by the human body, but diabetes develops when insulin stops working properly and glucose remains in the bloodstream. Heart disease, stroke, renal failure, blindness, nerve damage, gum disease, and even amputations can all be caused by hyperglycemia, or high blood sugar. In recent years, machine learning has made great strides, and its usage has improved numerous areas of healthcare. This research aimed to construct a model that could accurately predict a person's likelihood of developing diabetes. In this paper, we focus on preprocessing techniques and the problem of data imbalance. In this research, diabetes diagnosis was accomplished using the random forest classifier (RFC), WBSMOTE, and the wrapping method. Accuracy in the RFC was improved when evolutionary algorithms were used with the hyperparameter optimisation technique. The UCI machine learning repository's PIMA Indian Diabetes (PIDD) dataset was used for the tests. The outcomes demonstrated that the suggested method outperformed with a maximum accuracy of 93%.