Predicting the Relapse Category in Patients with Tuberculosis: A Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Analysis

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 relapse 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.