Accuracy Enhancement of Correlated Naive Bayes Method by Using Correlation Feature Selection (CFS) for Health Data Classification

The main problem of health datasets is having a lot of data attributes and irrelevant features, so that the computation of the classification method takes more time to solve it. The purpose of this study is to implement the CFS feature selection method to improve the accuracy of the Correlated Naive Bayes method. Therefore, there are some stages used in this study such as: collecting dataset of Pima Indian Diabetes, preprocessing data especially for transformation data, selection of CFS feature, classification, and then performance evaluation based on accuracy. Based on the test results using the 10-fold cross validation method, the best accuracy is about 69.4% compared without feature selection, it is obtained by a combination of Correlated Naive Bayes and CFS methods. Thus, the CFS feature selection method may increase the accuracy of the Correlated Naive Bayes method by 2.25%.