Background

Tuberculosis (TB) remains a critical public health threat in Uganda. Achieving the End TB targets 90% fewer deaths and 80% lower incidence by 2030 requires a shift from traditional surveillance to predictive, data-driven health intelligence. Machine Learning (ML) enables transformation of raw health data into real-time, actionable insights for smarter interventions.