Fuzzy-Based Predictive Analytics for Early Detection of Diabetes

Abstract

Today, diabetes is the most costly and burdensome chronic disease. The severity of diabetes is reducing with anticipation, premature recognition, and the early supervision impediments in people. These symptoms are the optimization of the diagnosis phase of the disease through the process of evaluating symptomatic characteristics and daily habits of patients. Big data analytical tools play a useful task in executing significant real-time investigation on the huge volumes of data and are also used to foresee the crisis situations earlier than it occurs. This chapter accomplished an efficient assessment of the applications of machine learning algorithms and tools in the diabetes investigation relating to genetic background and environment. With improving accuracy for early detection and prevention of diabetes, this chapter implemented a fuzzy linear and logistic regression model with fuzzy clustering for predicting early detection of diabetes.