Ml – Based Diabetes Foretell Using Svm & Logistic Regression in Healthcare

Abstract

Diabetes is one of the most grievous health condition in the world which has no remedy to cure it after a particular stage. Based on the survey of the last 20 years, the number of people having diabetes tripled. Over 422 million people in the world are diagnosed with diabetes. It is caused due to increased blood sugar level because of imbalance in insulin processing by the body, which leads to varieties of disorders like Coronary failure, blood pressure, etc. This paper mainly focuses on the management of diabetes prediction, that will be approached using ML algorithms. It provides better results in diabetes detection by constructing models from patient datasets. The aim of this work is to make a prediction of diabetes more precisely with Logistic Regression (binary classification) and Support Vector Machine algorithm (SVM) in machine learning. It predicts the diabetes risk in early stages using symptoms and also predict using distinctive attributes of diabetes. Therefore, two different datasets of patients are used to train the models. This project work will function as an aid for the medical examiners in the diagnosis of diabetes of the patients. Thus, it can significantly help diabetes research and, ultimately, improve the quality of healthcare for diabetic patients.