Tuberculosis (TB) is a disease that affects people around the world, especially people in underdeveloped countries. TB is one of the top ten causes of death globally so improvement in understanding diagnosis and treatment of TB affected patients could lead to major improvements in world health. This thesis research evaluated relapse patients specifically, deeming a relapse patient as one who has either been cured or completed their last treatment and then is diagnosed with TB again.
This research uses dynamic predictive modeling, based upon the random forest algorithm, to predict treatment outcomes for recurrent TB patients using demographic and follow-up clinical data. The model identifies variables and time periods that are significant in predicting whether the patient will be cured. The model is applied to data provided by the Evaluation System of National Control Program of Tuberculosis in the Republic of Moldova. Our results reveal insights that could be used by physicians to improve treatment strategy and monitor patients more effectively throughout the treatment trajectory
