Diabetic Prediction Using Hybrid Smote-Tree Big Data Classification with Artificial Neural Network

Abstract

Diabetes is one of the worst illnesses now plaguing humanity. The condition is caused by the body's abnormal reaction to insulin, a vital hormone that transforms sugar into the energy required for the normal functioning of daily living. In addition to increasing the chance of developing kidney disease, heart disease, and retinal eye disease, nerve damage, and blood vessel damage, diabetes causes serious consequences in the body. This research provides diabetes prognosis based on hybrid SMOTE-TREE large data categorization utilizing Artificial Neural networks (ANN). Artificial Neural Networks deliver promising results for nonlinear data. Hence ANN is picked for creating the model to predict diabetes among numerous ML (machine learning) techniques. The goal is to develop a decision support system to predict and diagnose diabetes with maximum accuracy, given the parameters. The parameters are set such that the best accuracy is obtained.