Enhancement of Performance of K-Nearest Neighbors Classifiers for the Prediction of Diabetes Using Feature Selection Method

For any classification or prediction problem, machine learning algorithm plays an important role in analysis of data. It enhances the performance capability of classifiers and ease the difficulties. In this research analysis, a prediction model is developed for diabetes disease using K-nearest neighbors classification algorithm. The model is implemented in Python using PIMA indian diabetes dataset.The objective of research work is to enhance the prediction capability of KNN classifier with the help of feature selection and normalization of data. A study is also performed to find the optimal number of neighbors on which KNN returns its best result. The F1 score is considered as the main performance metrics for this work.