Rule Based Approach for Prediction of Chronic Kidney Disease: A Comparative Study
Namrata Singh and Pradeep SinghDepartment of Computer Science and Engineering, National Institute of Technology, Raipur-492001, Chhattisgarh, India.
Corresponding Author E-mail: nsingh.phd2016.cs@nitrr.ac.in
Abstract: Chronic Kidney Disease (CKD) is a major public health problem with growing challenges for its early diagnosis, timely prevention and effective treatment. The present dataset on Chronic Kidney Disease consists of 24 predictive parameters. The study performs a comparative analysis of rule based classifiers inorder to generate human interpretable rules for diagnosing CKD. Various rule-based approaches for comparison that have been used in the paper are JRip, CART, Conjunctive Rule, C4.5, NNge, OneR, Ridor, PART, and Decision Table-Naive Bayes (DTNB) hybrid classifier. The study concludes that among all the conventional classifiers cited, DTNB is best rule-based classifier with highest area under ROC (0.999) along with lowest False Positive Rate (0.011).
Keywords: Chronic Kidney Disease; classification rules; data mining; predictive analysis rule-based classifiers; Back to TOC