Diabetes Classification with Healthcare System Application

Abstract

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on medical data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. This project also aims to propose an effective technique for earlier detection of the diabetes disease which is capable of giving diabetes care based on rule-based technique. Specifically, this system enables the user to select the symptoms that they have without having to see the doctor as part of early screening. Using these techniques, this patient can aware whether they are potentially at risk for diabetes or not. In the current version, this technique is capable to detect three possible outcomes which is healthy, Diabetic Type 1, and Diabetic Type 2.