Abstract
Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. This study aims to investigate the application of several machine learning models in predicting tuberculosis (TB) treatment outcomes. The study utilizes an extensive dataset from Karnataka, India. The study utilized advanced methodologies, such as XGBoost, to address the issue of imbalanced data and enhance the precision of the predictions. The findings show that these models can greatly improve the identification of patients who are likely to have poor adherence to their treatments, allowing for more focused interventions. The objective is to predict tuberculosis treatment outcomes and assist public health initiatives in Karnataka through the integration of these predictive technologies into the healthcare system.
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