Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.

Machine learning models, particularly Random Forest and Decision Tree, demonstrated strong predictive performance for traumatic brain injury outcomes using routinely collected variables in a resource-limited low- and middle-income countries setting.

Their interpretability and reliance on admission-level data make them potential tools for real-time triage and risk stratification.

Future research should focus on external validation and integration into clinical decision-support systems to support scaleup across similar settings.