Retrospective Analysis of Tuberculosis Treatment Duration and Outcomes Using Explainable Machine Learning on Malaysian Patient Data

Background/Objectives: This study utilizes machine learning (ML) models integrated with explainable artificial intelligence (XAI) to perform a retrospective analysis of factors associated with tuberculosis (TB) treatment duration and outcomes.

Methods

Two datasets comprising completed TB patient records from a Malaysian hospital were analyzed. Significant features were identified using Pearson correlation for treatment duration and the Boruta algorithm for treatment outcome. Support Vector Regressor (SVR) was used to evaluate treatment duration, while Logistic Regression, Random Forest, and XGBoost were evaluated for treatment outcome classification. SHapley Additive Explanations (SHAP) was integrated to enhance model interpretability.