Open Journal of Social Sciences, 2018, 6, 29-36 http://www.scirp.org/journal/jss ISSN Online: 2327-5960 ISSN Print: 2327-5952 DOI: 10.4236/jss.2018.612003 Dec. 14, 2018 29 Open Journal of Social Sciences Predicting the Relapse Category in Patients with Tuberculosis: A Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Analysis Arnold Peralta Dela Cruz Nueva Ecija University of Science and Technology, Cabanatuan City, Philippines Abstract Predicting the outcome of treatment among TB patients is a big concern of the Department of Health. Data mining in health care system can be used for decision making. The most widely used for data exploration is decision tree based on divide and conquer technique. The objectives of this article are to create a predictive data mining model for TB patient category to find the re- lapse treatment and to classify the factors influencing the relapse treatment to provide assistance, guidance, and appropriate warning to TB patients who are at risk. The dataset of TB patient records is verified and applied in CHAID classification tree algorithm using SPSS Statistics 17.0. The classification tree model identified the set of two statistically significant independent variables (DSSM Result, Age) as predictors of patient category. Keywords Data Mining, CHAID Algorithm, Decision Tree, Relapse, Tuberculosis 1. Introduction Philippine Tuberculosis (TB) is a foremost community health problem and re- mains a major cause of death and it is one of the nations with high TB incidence. “Philippines ranked ninth among the 22 high TB burdened countries” [1]. In 2015, 14,000 Filipinos died from tuberculosis and 4.8 million from this number are mostly poor [2]. TB is a treatable and preventable disease, yet many are still infected and are continuously suffering. In TB treatment, one major problem is guaranteeing patients to pursue their How to cite this paper: Dela Cruz, A.P. (2018) Predicting the Relapse Category in Patients with Tuberculosis: A Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Analysis. Open Journal of Social Sciences, 6, 29-36. https://doi.org/10.4236/jss.2018.612003 Received: November 9, 2018 Accepted: December 11, 2018 Published: December 14, 2018 Copyright © 2018 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access