Temporal Machine Learning Models for Classifying Suspected Dengue Cases in Mexico Using Surveillance Data from 2025

Background: Dengue fever remains a major public health challenge in Mexico, exhibiting pronounced seasonal behavior and substantial geographic heterogeneity. Using recent epidemiological surveillance data may improve predictive performance and better reflect the current epidemiological context. Objective: The aim of this study was to develop and compare temporal machine learning models for the binary classification of confirmed and negative dengue cases in Mexico using 2025 national surveillance data. Methods: A total of 68,222 suspected dengue cases reported in 2025 were analyzed. The outcome variable was CASE_STATUS, encoded as 0 for negative cases and 1 for confirmed cases. The dataset was divided chronologically into training (January–September), validation (October), and testing (November–December) subsets. Nine machine learning algorithms were evaluated: Random Forest, Bayesian Network, XGBoost, CatBoost, Naïve Bayes, Logistic Regression, Multilayer Perceptron, Support Vector Machine, and LightGBM. Preprocessing included scaling, encoding, age discretization for Bayesian Network, class imbalance treatment, and model-specific feature-importance analyses. Performance was assessed using accuracy, Precision, Recall, F1-score, ROC-AUC, and PR-AUC. Results: Random Forest achieved the best overall performance, with the highest test F1-score (0.7254) and PR-AUC (0.7300) at an optimized threshold of 0.397, together with a high Recall (0.8938). Bayesian Network achieved the highest test accuracy (0.7023) and ROC-AUC (0.7756), although its overall operational balance was less favorable considering class imbalance. Geographic and institutional variables were the most influential predictors across models, whereas comorbidities generally contributed less. Conclusions: Temporal machine learning models are useful for dengue case classification in Mexico, and Random Forest was the most robust approach, balancing sensitivity and overall predictive performance. From an operational perspective, this finding is especially relevant in dengue surveillance, where failure to identify true confirmed cases may have important public health consequences.