Predicting tuberculosis treatment relapse among geriatric patient using machine learning algorithms in a high Tuberculosis-burden Ghanaian setting.
This study demonstrates that a Random Forest machine learning model outperforms SVM, DNN, and Logistic Regression algorithms in predicting tuberculosis treatment relapse among geriatric patients in Ghana with 99% accuracy, leading to recommendations for its pilot integration into local clinic workflows and improved data-sharing policies.