Abstract
Tuberculosis remains an important problem in public health that threatens the world, including the Philippines. Treatment relapse continues to place a severe problem on patients and TB programs worldwide. A significant reason for the development of decline is poor compliance with medical treatments. The objectives of this research are to generate a predictive data mining model to classify the treatment relapse of TB patients and to identify the features influencing the category of treatment relapse. The TB patient dataset is applied and tested in decision tree J48 algorithm using WEKA. The J48 model identified the three (3) significant independent variables (DSSM Result, Age, and Sex) as predictors of category treatment relapse.
Highlights
Tuberculosis (TB) remains the deadliest infectious disease worldwide, with 10.4 million infections and a death toll of 1.7 million people in 2016, according to the World Health Organization (WHO) statistics [1]
Decision Tree J48 is the implementation of algorithm ID3 (Iterative Dichotomiser 3) developed by the WEKA project team
The J48 classifier was developed by the researchers using WEKA and trained it on a preprocessed TB dataset
Summary
Tuberculosis (TB) remains the deadliest infectious disease worldwide, with 10.4 million infections and a death toll of 1.7 million people in 2016, according to the World Health Organization (WHO) statistics [1]. Tuberculosis is an infectious disease caused by a bacterium called Mycobacterium Tuberculosis that remains a global health problem. It primarily infects the lungs, bones, lymph, and digestive organs. Lower immunity, who use immunosuppressive drugs, old age, and people with HIV/AIDS infection, are more likely to develop TB [2]. In the Philippines, TB is still a major public health concern. The Philippines ranked ninth (9th) among 22 TB high burdened countries (HBCs). One major problem in TB treatment is guaranteeing the patients to follow their
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