Heldal, E., Docker, H., Caugant, D.A., et al. (2000) Pulmonary Tuberculosis in Norwegian Patients. The Role of Reactivation, Re-Infection and Primary Infection Assessed by Previous Mass Screening Data and Restriction Fragment Length Polymorphism Analysis. International Journal of Tuberculosis and Lung Disease, 4, 300e7.

has been cited by the following article:

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

  AUTHORS: Arnold Peralta Dela Cruz

  KEYWORDS: Data Mining, CHAID Algorithm, Decision Tree, Relapse, Tuberculosis

  JOURNAL NAME: Open Journal of Social Sciences, Vol.6 No.12, December 14, 2018

  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.