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

Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes