Diabetes Prediction Using Knn ML

Diabetes is one of the major ailments and many people suffer from it. Many complications occur when diabetes remains untreated and undiagnosed. People with Sugar are at greater risk for diseases such as heart problem, kidney problem, stroke, eye vision, nerve damage, and many more. The annoying effects of the diagnostic procedure on visiting the affected person at the diagnostic center and seeing a doctor. The reason for this study is to develop a version that can predict the risk of diabetes in more precise patients. three algorithms for the decision-making gadget class that Tree, SVM, and Naive Bayes are used in this test to diagnose diabetes early. therefore, early diabetes prognosis is very important. In this paper, we use system-controlled control algorithms such as Vector Assistant Machine (SVM), Naive Bayes classifier to educate the actual numbers of 520 diabetic patients and 16- to 90-year-old diabetic patients. with comparative testing of type and accuracy of recognition, the overall performance of the assist vector gadget is impressive.