Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes

TL;DR

This paper presents a hybrid stacked ensemble approach combined with genetic algorithms to improve the accuracy of diabetes prediction using real Indian diabetic data, achieving over 98% accuracy.

Contribution

The study introduces a novel ensemble method optimized with genetic algorithms for diabetes prediction, enhancing diagnostic accuracy over existing models.

Findings

Achieved 98.8% accuracy in diabetes diagnosis.

Demonstrated the effectiveness of genetic algorithms in optimizing ensemble models.

Validated the approach on real Indian diabetic data.

Abstract

Diabetes is currently one of the most common, dangerous, and costly diseases in the world that is caused by an increase in blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people's health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people's health without compromising their comfort is an essential need. In this study, the Ensemble training methodology based on genetic algorithms are used to accurately diagnose and predict the outcomes of diabetes mellitus. In this study, we use the experimental data, real data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.