Application of machine learning for early detection of chronic diseases in Africa.

Background: Chronic diseases such as diabetes, hypertension, and cardiovascular conditions continue to burden African public health systems, especially due to late diagnosis. This study explores the application of Machine Learning (ML) for the early detection of diabetes using a localized dataset of 768 electronic health records from a clinic in Africa.

Design and methods: A Design Science Research methodology was used to evaluate and compare different ML algorithms which includedDecision Trees, Support Vector Machines, Naïve Bayes, and a Neural Network (NN). preprocessing and hyperparameter tuning was applied to optimized the model perfomance. The models were tested for feasibility in edge-based deployment scenarios which are ideal for implimentation in the African setting.

Results: The optimized NN model achieved the highest accuracy (89%), minimal latency (1 ms), and low memory usage (1 kB RAM), making it suitable for deployment in resource-constrained environments. While the dataset is limited in scope, it sets a foundation for future cross-regional studies.

Conclusion: This study demonstrates the potential of edge-deployable ML models in supporting early chronic disease detection in Africa and recommends future work in regulatory alignment, ethical safeguards, and multi-site validations.