Normalized Naïve Bayes Model to predict Type –2 Diabetes Mellitus

Abstract

Diabetes Mellitus is a serious illness that distresses a large number of people all over the world. Diabetes Mellitus may be caused by age, obesity, lack of exercise, genetic diabetes, lifestyle, poor diet, high blood pressure, and other factors. Diabetics are at a greater risk of contracting conditions such as heart failure, kidney disease, stroke, eye disorders, nerve damage, and so on. The current hospital procedure is to gather necessary information for diabetes diagnosis via different tests, and then offer appropriate care based on the diagnosis. In the healthcare industry, machine learning and deep learning play a significant role. Databases in the healthcare industry are huge. Data analytics can be used to examine large databases and uncover secret information and trends, allowing users to gain insight from the data and forecast outcomes accordingly. The classification and prediction accuracy of the current system is not very good. Normalized Naïve Bayes (NNB) model is proposed in this paper, and its performances are compared in terms of different parameters to help with classification. RapidMiner Studio is used to carry out the execution.