DIABETES PREDICTION USING SUPPORT VECTOR MACHINES

Abstract

One of the worst illnesses in the world is diabetes. It is also the creator of a variety of other diseases, such as urinary organ illness, blindness, and cardiac failure. The patient must go to a diagnostic facility in this situation to receive their reports following consultation. Because of this, they always have to invest both money and time. However, as machine learning techniques have improved, we now have the freedom to look for the right solution. For example, we now have sophisticated information processing systems that can predict whether a patient has polygenic disease or not. Additionally, anticipating the illness early results in giving the patients what they need before it becomes urgent.Goals of this analysis is to develop a system which predicts the diabetes risk level of patient.The experimental results shows that the prediction of diabetes done at high accuracy using support vector machines. KEYWORDS: Early Detection, Machine Learning, SVM(Support Vector Machines), Accuracy.