Type 2 Diabetic Prediction Using Machine Learning Algorithm
Diabetes mellitus is one of the most important chronic disease and has become a major public health challenge in the recent world. Currently Machine Learning approaches have been used to analyze and predict the probability of people getting affected by diabetes. Diabetes can be effectively identified using the proposed Machine Learning technique. Many techniques and algorithms were used before for the prediction of type 2 diabetes prediction ,one such model was Multinomial Logistic Regression .Moving a step ahead to improve the diagnostic efficiency, this paper proposes the use of Weighted K –Nearest neighbor for detecting the type-2 Diabetes. This new approach proves higher effectiveness when compared to Multinomial Logistic Regression. Using Pima Indian Dataset the experiments were performed and it shows that efficiency is higher for weighted KNN when compared to Multinomial Logistic Regression.
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