Hybrid Machine Learning Algorithms Health Database for Diabetes Prediction using MLP and SVM

The presence of persistently increased blood glucose levels in diabetic individuals serves as a diagnostic indicator of the condition. Diabetes mellitus type 2 is characterised by the inability of cells inside the body to make use of insulin that has been created. This is the hypothesis put out by medical professionals who study type 2 diabetes. The human organ known as the pancreas, which is responsible for producing insulin, may become unable to do its job properly, which can lead to the development of diabetes mellitus type 1. There has been a huge increase in the number of extremely sensitive health data as a direct result of the improvements that have been made in biotechnology as well as the strengthening of the public healthcare system. Intelligent data analysis technologies have revealed a surprising number of patterns. These technologies were built with the intention of preventing a variety of severe diseases. Diabetes mellitus puts a person at a much greater risk of developing cardiovascular disease, kidney disease, and nerve damage, all of which may significantly shorten a person's life expectancy. In this study, a hybrid learning algorithm is formed by combining the MLP multilayer perceptron method with the SVM support vector machine algorithm. This results in improved accuracy in diabetes identification