Machine learning model for predicting tuberculosis co-infection risk among high-risk populations including HIV-Positive individuals

Machine learning (ML) offers transformative potential in public health by enabling predictive modeling of complex disease interactions, including co-infections. Tuberculosis (TB) remains a major global health challenge, with co-infection among high-risk populations—particularly HIV-positive individuals—significantly increasing morbidity and mortality. Early identification of individuals at heightened risk of TB co-infection is critical for timely interventions, targeted screening, and optimized clinical management. This presents a machine learning framework for predicting TB co-infection risk among high-risk populations, leveraging demographic, clinical, immunological, and behavioral data to enhance predictive accuracy. The model incorporates key predictors, including HIV viral load, CD4 counts, antiretroviral therapy adherence, socioeconomic factors, comorbidities, and exposure-related variables. Feature engineering and selection methods are employed to identify the most informative variables, while class imbalance techniques address the relative rarity of TB co-infection events. Supervised machine learning algorithms, including random forests, gradient boosting, and logistic regression ensembles, are applied to training datasets drawn from national health registries, cohort studies, and electronic medical records. Model performance is evaluated through cross-validation, with metrics including area under the receiver operating characteristic curve (AUC), precision, recall, and F1 score to ensure robustness and generalizability. Preliminary findings indicate that integrating clinical and sociodemographic data substantially improves risk stratification, enabling healthcare providers to prioritize screening and preventive therapy for high-risk individuals. Additionally, the model demonstrates the potential to uncover non-linear relationships and interactions between immunological and behavioral factors that may be overlooked in traditional statistical analyses. Beyond predictive accuracy, the framework emphasizes interpretability, transparency, and ethical considerations, ensuring that predictions support clinical decision-making without reinforcing biases or inequities in care. Machine learning models offer a promising approach for anticipating TB co-infection risk among vulnerable populations, particularly HIV-positive individuals. By combining high-dimensional data with advanced predictive algorithms, these models can inform proactive public health strategies, optimize resource allocation, and ultimately reduce TB-related morbidity and mortality. The framework underscores the importance of integrating data-driven tools into targeted interventions and surveillance systems for high-risk groups. Keywords: Tuberculosis, HIV, TB/HIV Co-Infection, High-Risk Populations, Predictive Modeling, Machine Learning, Risk Factors, Clinical Data, Demographic Data, Epidemiology.