Machine learning for predicting severe dengue, Puerto Rico Article Swipe

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Zachary J. Madewell , Dania M. Rodríguez , Maile T. Phillips , Vanessa Rivera‐Amill , Gabriela Paz–Bailey , Laura E. Adams , Joshua M. Wong · YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1101/2024.11.15.24317377

Background Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. Traditional warning signs recommended by the World Health Organization (WHO) offer a practical approach for clinicians but have limitations in sensitivity and specificity. This study evaluates the performance of machine learning (ML) models compared to WHO- recommended warning signs in predicting severe dengue among laboratory-confirmed cases in Puerto Rico. Methods We analyzed data from Puerto Rico’s Sentinel Enhanced Dengue Surveillance System (May 2012–August 2024), using 40 clinical, demographic, and laboratory variables. Nine ML models, including Decision Trees, K-Nearest Neighbors, Naïve Bayes, Support Vector Machines, Artificial Neural Networks, AdaBoost, CatBoost, LightGBM, and XGBoost, were trained using 5-fold cross-validation and evaluated with area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). 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 a value closer to 1.0 reflects better performance. Results Among the 1,708 laboratory-confirmed dengue cases, 24.3% were classified as severe. Gradient boosting algorithms achieved the highest predictive performance, with AUC-ROC values exceeding 94% for CatBoost, LightGBM, and XGBoost. Feature importance analysis identified hemoconcentration (≥20% increase during illness or ≥20% above baseline for age and sex), leukopenia (white blood cell count <4,000/mm³), and timing of presentation to a healthcare facility at 4–6 days post-symptom onset as key predictors. Excluding hemoconcentration and leukopenia did not significantly affect model 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 ≥3 warning signs improved specificity (80.9%) while maintaining moderate sensitivity (78.6%), resulting in an AUC-ROC of 74.0%. Conclusions 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’ applicability in resource-limited settings, where access to laboratory data may be limited.

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preprint

Language

en

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https://doi.org/10.1101/2024.11.15.24317377

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green

References

44

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10

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https://openalex.org/W4404404898

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https://openalex.org/W4404404898

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DOI

https://doi.org/10.1101/2024.11.15.24317377

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Title

Machine learning for predicting severe dengue, Puerto Rico

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preprint

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en

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2024

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2024-11-15

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Zachary J. Madewell, Dania M. Rodríguez, Maile T. Phillips, Vanessa Rivera‐Amill, Gabriela Paz–Bailey, Laura E. Adams, Joshua M. Wong

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https://doi.org/10.1101/2024.11.15.24317377

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Yes

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green

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https://doi.org/10.1101/2024.11.15.24317377

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Dengue fever, Artificial intelligence, Geography, Psychology, Computer science, Virology, Medicine

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0

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44

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10

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