Comparative analysis in the prediction of early-stage diabetes using multiple machine learning techniques

Abstract

Diabetes is caused by high levels from blood glucose and it is characterized as a chronic disease, and also will disrupt fat and protein absorption.
The levels rise from blood glucose because it cannot be burned in the cells from the pancreas because of the deficiency of insulin secretion or the insulin produce by the cell are insufficient. By means of early detection, it may decrease the hazards and frequency of diabetes. The application of technology has been an essential part of providing accurate and acceptable results in the prevention and early detection of the illness. This research provided the best machine learning used for predicting the early stage of diabetes. The methods involve the feature selection or dimension reduction using relief-based filter (reliefF), tenfold cross-validation for testing and training data, and different machine learning classifiers such as the random forest (RF), support vector machines (SVM), and neural network (NN) is used. in this research,
RF recorded the highest precision point at 98.5%, which was able to provide a higher evaluation in terms of accuracy followed by SVM at 96.6% and NN at 96.2%. The results generated from this experiment are essential in contributing a new way that is highly accurate in determining diabetes
among patients.