Predictive Artificial Intelligence Models for Non-Communicable Disease Burden Forecasting in Africa

Background: Non-communicable diseases (NCDs) are rapidly emerging as the dominant cause of death in Africa, projected to surpass infectious diseases by 2030. Artificial intelligence (AI) and machine learning (ML) tools offer significant potential for forecasting disease burden, enabling early interventions and data-driven health system responses. However, the landscape of AI-based predictive modeling for NCD forecasting in Africa remains poorly defined. This study systematically mapped existing literature on AI and ML models used for NCD burden prediction across the continent to identify methodological trends, geographical coverage, and research gaps. Results: A total of 127 studies met inclusion criteria from 8,547 screened records. Most studies (68.5%) developed individual-level risk prediction models rather than population-level burden forecasts. Cardiovascular diseases (42.5%) and diabetes mellitus (38.6%) dominated the evidence base, while cancer (10.2%) and chronic respiratory diseases (8.7%) were underrepresented. Research activity was highly concentrated in South Africa, Kenya, and Nigeria, with 32 African countries unrepresented. The majority of models used supervised learning techniques such as random forests, support vector machines, and gradient boosting methods. Only 23.6% of studies applied temporal or longitudinal forecasting approaches, and external validation was reported in just 18.1% of cases. Most datasets were hospital-based (51.2%), with limited integration of multiple data sources to improve generalizability. Conclusions: AI-driven predictive modelling for NCD forecasting in Africa is expanding but remains methodologically fragmented and geographically uneven. The dominance of cross-sectional prediction over temporal forecasting limits its value for long-term planning. There is an urgent need for collaborative, multicounty research that incorporates diverse African data, standard validation frameworks, and integration into health system infrastructures. Strengthening local capacity for AI development and governance will be essential to ensure equitable and context-appropriate digital health innovation across the continent.