Machine Learning for Improved Dengue Diagnosis in Puerto Rico
Machine learning models, especially XGBoost and LightGBM, show promise for improving diagnostic accuracy for dengue using widely accessible clinical data, even in resource-limited settings. Future research should focus on developing user-friendly tools, such as mobile apps, web-based platforms, or clinical decision systems integrated into electronic health records, to implement these models in clinical practice and exploring their application for predicting dengue.
Authors
- Melissa Marzán‐Rodríguez (ORCID: https://orcid.org/0000-0003-3553-2675)
- Joshua M. Wong (ORCID: https://orcid.org/0000-0002-1062-0049)
- Maile T. Phillips (ORCID: https://orcid.org/0000-0001-6952-0440)
- Gabriela Paz‐Bailey (ORCID: https://orcid.org/0000-0001-8478-4911)
- Jomil Torres Aponte
- Zachary J. Madewell (ORCID: https://orcid.org/0000-0002-5667-9660)
- Vanessa Rivera‐Amill (ORCID: https://orcid.org/0000-0002-0289-3135)
- Laura E. Adams (ORCID: https://orcid.org/0000-0003-4698-7720)
- Dania M. Rodríguez (ORCID: https://orcid.org/0000-0003-2837-3629)
Institutions
- Centers for Disease Control and Prevention (US)
- Ponce Health Sciences University (PR)
- Puerto Rico Department of Health (PR)
Publication Details
- Journal
- Tropical Medicine & International Health
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1111/tmi.70036
- Citations
- 1
- Primary Topic
- Mosquito-borne diseases and control
- Type
- article
- Field-Weighted Citation Impact
- 1.88