Machine learning for improved dengue diagnosis, Puerto Rico
Authors: Madewell Zachary J. , Rodriguez Dania M. , Thayer Maile B. , Rivera-Amill Vanessa , Aponte Jomil Torres , Marzan-Rodriguez Melissa , Paz-Bailey Gabriela , Adams Laura E. , Wong Joshua M. 
Published: November 2024

AbstractBackgroundDiagnosing dengue accurately, especially in resource-limited settings, remains challenging due to overlapping symptoms with other febrile illnesses and limitations of current diagnostic methods. This study aimed to develop machine learning (ML) models that leverage readily available clinical data to improve diagnostic accuracy for dengue, potentially offering a more accessible and rapid diagnostic tool for healthcare providers.MethodsWe used data from the Sentinel Enhanced Dengue Surveillance System (SEDSS) in Puerto Rico (May 2012—June 2024). SEDSS primarily targets acute febrile illness but also includes cases with other symptoms during outbreaks (e.g., Zika and COVID-19). ML models (logistic regression, random forest, support vector machine, artificial neural network, adaptive boosting, light gradient boosting machine [LightGBM], and extreme gradient boosting [XGBoost]) were evaluated across different feature sets, including demographic, clinical, laboratory, and epidemiological variables. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), where higher AUC values indicate better performance in distinguishing dengue cases from non-dengue cases.ResultsAmong 49,679 patients in SEDSS, 1,640 laboratory-confirmed dengue cases were identified.□The□XGBoost and LightGBM models achieved the highest diagnostic accuracy, with AUCs exceeding 90%, particularly with comprehensive feature sets. Incorporating predictors such as monthly dengue incidence, leukopenia, thrombocytopenia, rash, age, and absence of nasal discharge significantly enhanced model sensitivity and specificity for diagnosing dengue. Adding more relevant clinical and epidemiological features consistently improved the models’ ability to correctly identify dengue cases.ConclusionsML 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.Author summaryDengue is a tropical disease caused by the dengue virus, which is transmitted by mosquitoes. It affects millions of people worldwide every year, leading to severe illness and even death in some cases. Accurate and timely diagnosis of dengue is crucial for proper treatment and controlling the spread of the virus. Traditionally, diagnosing dengue relies on symptoms and laboratory tests, which can sometimes be non-specific and not immediately available in distinguishing dengue from other similar illnesses. In our study, we explored the use of machine learning, a type of artificial intelligence, to improve dengue diagnosis using patient information from Puerto Rico. Our models, which use information like age, symptoms, and specific blood cell counts, can accurately predict whether someone has dengue. We found that some simple information, like whether a patient has a rash or low blood cell counts, can be very helpful in making a diagnosis. While more complex models performed slightly better, simpler models can also be effective, especially in places with limited resources. Our study shows that using computer models can improve dengue diagnosis and help healthcare providers make better decisions for their patients.