Early-Stage Detection of Diabetes Using Exploratory Machine Learning Algorithms

Abstract

Diabetes is an ongoing illness and can be severe enough to cause a standard medical emergency. According to the International Diabetes Federation more than 300 million people are affected by this disease across the globe . By the late 2030s, this disease will affect more than 600 million people worldwide . Diabetes is caused due to long term high blood sugar levels or blood glucose . According to various tests, there are several common techniques available for detecting  diabetes. Be that as it may, the early detection of diabetes is a very tedious task due to its complex dependence on various variables as it can affect organs such as the eye,kidney,heart and many others. AI is an upcoming field and it deals with the way a machine reaches a conclusion or a result based on the information fed to it. The aim of this project is to develop a framework that can more accurately identify the early chances of diabetes in a patient by linking the results of various AI methods. This task plans to foresee diabetes by means of three distinctive directed AI techniques including: SVM, Logistic relapse, ANN. This venture –––likewise expects to propose a powerful procedure for prior identification of the diabetes sickness.