Selection of Best K of K-Nearest Neighbors Classifier for Enhancement of Performance for the Prediction of Diabetes

Today, getting a meaningful information from an ocean of data obtained from numerous sources becomes very tedious work. A number of methods are applied to analyses various types of results obtained from them. Classification is one of them. Machine learning algorithms are used to train classifiers and enhance the capability of different classification methods. In this research paper, focus is given on K—nearest neighbor algorithm and has been analyzed its performance to classify patients in diabetic and non-diabetic class using Pima Indian Diabetes Dataset obtained from UCI repository. Diabetes mellitus, which is a metabolic disorder of human body, is one of the major health threats in world. Due to it, body becomes unable to consume insulin properly released by pancreas gland. Experimental analysis has been performed on PIMA dataset using python. A detail study is made on it using KNN algorithm with different values of K and identified the best value of K on which KNN returns best result. KNN provide a better accuracy 81.17% after applying feature selection method.