Machine Learning for Predicting Health Council Decision of Return-to-Work at <i>t</i> Months for Tuberculosis Patients

Predicting the exact duration of sick leave in patients with tuberculosis remains challenging due to the heterogeneity of recovery trajectories. This study uses machine learning to estimate sick leave duration at t months (t = 6, 9, 12) by integrating post-treatment radiographic progression and key clinical and sociodemographic factors. This is a retrospective study of tuberculosis patients with documented sick leave duration (2019-2021) presented to the Health Council by the Pulmonary and Phthisiology Department of the Cocody University Hospital. The methodological approach of this study also differs from previous work by the identification of innovative predictive factors, the use of pulmonary sequelae data as a dynamic marker, the provision of individualized predictions that can be updated with new radiographs, and the comparison of the relative impact of variables such as Human Immunodeficiency Virus and type of employment. Also, the personalization of predictions through fine patient stratification and dynamic recommendations adjusting predictions according to treatment progress are major contributions. This approach guarantees a rigorous, clinically relevant, and actionable evaluation for decision-makers and would allow us to assess both the technical performance of the models used and their clinical interpretability, while highlighting the most predictive factors of the duration of work stoppage.