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World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limited sensitivity and specificity. This study aims to evaluate machine learning (ML) model performance compared to WHO-recommended warning signs in predicting severe dengue among laboratory-confirmed cases in Puerto Rico.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed data from Puerto Rico\u2019s Sentinel Enhanced Dengue Surveillance System (May 2012\u2013August 2024), using 40 clinical, demographic, and laboratory variables. Nine ML models, including Decision Trees, K-Nearest Neighbors, Na\u00efve Bayes, Support Vector Machines, Artificial Neural Networks, AdaBoost, CatBoost, LightGBM, and XGBoost, were trained using fivefold cross-validation and evaluated with area under the receiver operating characteristic curve (AUC-ROC), sensitivity, and specificity. A subanalysis excluded hemoconcentration and leukopenia to assess performance in resource-limited settings. An AUC-ROC value of 0.5 indicates no discriminative power, while values closer to 1.0 reflect better performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the 1708 laboratory-confirmed dengue cases, 24.3% were classified as severe. Gradient boosting algorithms achieved the highest predictive performance, with an AUC-ROC of 97.1% (95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 96.0\u201398.3%) for CatBoost using the full 40-variable feature set. Feature importance analysis identified hemoconcentration (\u2265\u200920% increase during illness or\u2009\u2265\u200920% above baseline for age and sex), leukopenia (white blood cell count\u2009&lt;\u20094000\/mm\n                      <jats:sup>3<\/jats:sup>\n                      ), and timing of presentation at 4\u20136\u00a0days post-symptom onset as key predictors. When excluding hemoconcentration and leukopenia, the CatBoost AUC-ROC was 96.7% (95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 95.5\u201398.0%), demonstrating minimal reduction in performance. Individual warning signs like abdominal pain and restlessness had sensitivities of 79.0% and 64.6%, but lower specificities of 48.4% and 59.1%, respectively. Combining\u2009\u2265\u20093 warning signs improved specificity (80.9%) while maintaining moderate sensitivity (78.6%), resulting in an AUC-ROC of 74.0%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML models, especially gradient boosting algorithms, outperformed traditional warning signs in predicting severe dengue. Integrating these models into clinical decision-support tools could help clinicians better identify high-risk patients, guiding timely interventions like hospitalization, closer monitoring, or the administration of intravenous fluids. The subanalysis excluding hemoconcentration confirmed the models\u2019 applicability in resource-limited settings, where access to laboratory data may be limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s40249-025-01273-0","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T11:21:10Z","timestamp":1738668070000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine learning for predicting severe dengue in Puerto Rico"],"prefix":"10.1186","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5667-9660","authenticated-orcid":false,"given":"Zachary J.","family":"Madewell","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dania M.","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maile B.","family":"Thayer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Rivera-Amill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriela","family":"Paz-Bailey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura E.","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua M.","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,4]]},"reference":[{"issue":"7446","key":"1273_CR1","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1038\/nature12060","volume":"496","author":"S Bhatt","year":"2013","unstructured":"Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. 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