DIABETES PREDICTION USING MACHINE LEARNING

Abstract

Nowadays most of the people is suffering from diabetes. Diabetes is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the body’s cells do not respond properly to insulin. The constant hyperglycemia of diabetes is related to long-haul harm, brokenness, and failure of various organs, particularly the eyes, kidneys, nerves, heart, and veins. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes which will help in detection of diabetes in the patients before it becomes fatal. The proposed system focuses using algorithms combinations shown above in the block diagram The base classification algorithms are: Decision tree, Random forest, Support Vector Machine, Logistic Regression, KNN for accuracy authentication. Here we are using Machine Learning Algorithms to predict the data and the algorithms we will use are: Decision Tree, Random Forest, Logistic Regression , SVM Algorithm, KNN Algorithm The proposed approach will use different classification and ensemble methods and implemented using python. These methods will be standard Machine Learning methods used to obtain the best accuracy from data. Overall we will use best Machine Learning techniques for prediction and to achieve high performance accuracy.The main aim of this project is to design and implement Diabetes Prediction Using Machine Learning Methods and Performance Analysis of that methods. It uses various classification and ensemble learning method in which SVM, Knn, Random Forest, Decision Tree, Logistic Regression used.