Web Based Diabetes Prediction System Using Machine Learning

Abstract

Diabetes is a critical condition that affects a large number of people. It can be caused by age, obesity, lack of exercise, genetic diabetes, lifestyle, poor diet, high blood pressure, and other factors. People with diabetes have an increased risk of developing heart disease, kidney disease, stroke, eye problems, nerve damage, and more. The current practice of the hospital is to gather the necessary information for the diagnosis of diabetes using various tests, and then to provide appropriate treatment according to the diagnosis. Big Data Analytics is important in the healthcare industry. Data stored in the healthcare industry is huge in size. By using large data statistics, one can scan large data sets to reveal hidden information and trends, enabling one to obtain data and predictably produce results accordingly. The classification and prediction accuracy of the current method is not very good. In this study, we present a predictive model of diabetes for advanced diabetes classification that includes a few external variables that cause diabetes in addition to normal components such as glucose, BMI, age, insulin, and so on. Compared to the old database, the new database improves the accuracy of categories. In addition, a diabetic pipeline model was developed for the purpose of improving the accuracy of the sections.