Ensemble Machine Learning Approach for Diabetes Prediction

Abstract

The technological advancements applied in the area of healthcare systems helps to meet the requirement of increasing global population. Due to the infections by the various microorganisms, people around the world are affected with different types of life-threatening diseases. Among the different types of commonly existing diseases, diabetes remains the deadliest disease. Diabetes is a major cause for the change in all physical metabolism, heart attacks, kidney failure, blindness, etc. Computational advancements help to create health care monitoring systems for identifying different deadliest diseases and its symptoms. Advancements in the machine learning algorithms are applied in various applications of the health care systems which automates the working model of health care equipment’s and enhances the accuracy of disease prediction. This work proposes the ensemble machine learning based boosting approaches for developing an intelligent system for diabetes prediction. The data collected from Pima Indians Diabetes (PID) database by national institute of diabetes from 75664 patients is used for model building. The results show that the histogram gradient boosting algorithms manages to produce better performance with minimum root mean square error of 4.35 and maximum r squared error of 89%. Proposed model can be integrated with the handheld biomedical equipment’s for earlier prediction of diabetes.