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Despite the growing adoption of machine learning in healthcare, gaps persist in deploying models as scalable, real-world solutions. This study developed an end-to-end machine learning framework to predict diarrhea among children under five in SSA, integrating rigorous model development with Flask-based deployment for practical use. Using nationally representative Demographic and Health Surveys (DHS) data from 27 SSA countries (2016-2024), we preprocessed data (handling missing values, feature selection, and SMOTE for class imbalance), trained a Random Forest classifier (optimized via RandomizedSearchCV), and deployed the model as a RESTful API with Flask. The final model demonstrated strong predictive power, with 79.6% accuracy and a particularly high recall of 84.1%, meaning it is exceptionally effective at identifying true diarrhea cases. Most importantly, the model is no longer just a research output; it is a deployed, interactive system ready for practical application. This work successfully demonstrates a complete pipeline from data to deployment, offering a tangible solution that can aid public health decision-making. We have proven that it is possible to close the gap between machine learning research and real-world implementation. To build on this foundation, future work should focus on enhancing the model\u2019s interpretability for health workers, adopting more scalable deployment technologies like FastAPI and Docker, and conducting rigorous field validation with community stakeholders to ensure these tools truly meet the needs of those they are designed to serve.<\/jats:p>","DOI":"10.1038\/s41598-026-43140-4","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T16:25:35Z","timestamp":1773419135000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deployment of a machine learning-based predictive system for childhood diarrhea in Sub-Saharan Africa"],"prefix":"10.1038","volume":"16","author":[{"given":"Eliyas Addisu","family":"Taye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyob Akalewold","family":"Alemu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Halima Ayalew","family":"Kebede","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen Brhan","family":"Alemaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simachew Getaneh","family":"Endalamew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofiya Ayalew","family":"Kebede","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Solomon Keflie","family":"Assefa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adem Tsegaw","family":"Zegeye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belayneh Jejaw","family":"Abate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dejen Kahsay","family":"Asgedom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Endalew Minwuye","family":"Andargie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"43140_CR1","doi-asserted-by":"crossref","unstructured":"Sarker, I. 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Formal permission to access the data was granted by submitting the study title and objectives via the official DHS platform (\n                      \n                      ). The DHS Program adheres to rigorous ethical standards, with review and approval by both the host country\u2019s institutional review board and the ICF International Institutional Review Board. As this study involved secondary analysis of fully de-identified data, no direct participant involvement or consent was required. For queries regarding ethics approval, the DHS Program can be contacted at dhs@dhsprogram.com.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No patients or members of the public were directly involved in the design, conduct, reporting, or dissemination of this research, as it utilized secondary, de-identified DHS datasets.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Patient and public involvement"}}],"article-number":"15199"}}