Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models

ABSTRACT

Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patient is likely to leave the treatment incomplete, leading to a more lethal, drug resistant form of disease. The World Health Organization (WHO) propagates Directly Observed Therapy Short-course (DOTS) as an effective way to stop the spread of TB in communities with a high burden. But DOTS also adds a significant burden on the financial feasibility of the program. We aim to facilitate TB programs by predicting the outcome of the treatment of a particular patient at the start of treatment so that their health workers can be utilized in a targeted and cost-effective way. The problem was modeled as a classification problem, and the outcome of treatment was predicted using state-of-art implementations of 3 machine learning algorithms. 4213 patients were evaluated, out of which 64.37% completed their treatment. Results were evaluated using 4 performance measures; accuracy, precision, sensitivity, and specificity. The models offer an improvement of more than 12% accuracy over the baseline prediction. Empirical results also revealed some insights to improve TB programs. Overall, our proposed methodology will may help teams running TB programs manage their human resources more effectively, thus saving more lives.

KEYWORDS:

• Predicting tuberculosis outcome
• optimization of health workers
• ehealth
• predictive analysis
• tuberculosis treatment
• drug-susceptible tuberculosis

Acknowledgments

The authors would like to express their gratitude to Dr. Naila Baig Ansari, Institutional Review Board, and Interactive Research & Development, for reviewing the abstract and providing patient data. Furthermore, we would like to thank Dr. Amyn Malik, Emory University for his guidance and insights. Furthermore, special thanks to Dr. Asad Zaidi, Interactive Research & Development for his valuable feedback.

Declaration of Interest

The authors report no conflict of interests.