Early Dengue Prediction in Bangladesh: A Comparative Study With Feature Analysis, Explainable Artificial Intelligence, and Model Optimization

Dengue fever presents a growing public health challenge in tropical and subtropical regions, where early detection is crucial for effective intervention. This study conducts a comprehensive comparative analysis of 13 machine learning and deep learning models for nonclinical, symptom-based dengue prediction, focusing on the Bangladeshi population. Using a dataset of 500 patient records with 22 symptom-based features, we evaluated a wide spectrum of classifier algorithms, including tree-based (e.g., random forest, extra trees, bagging), linear (logistic regression, SGDClassifier), and an instance-based classifier. Our comparative evaluation revealed that a custom-built, hyperparameter-tuned artificial neural network (ANN) achieved the highest accuracy of 97.5%, significantly outperforming all other models. While tree-based models like random forest also demonstrated strong performance (93.2%), other classifiers showed considerably lower efficacy. To ensure transparency in our top-performing model, SHapley Additive exPlanations (SHAP) was employed, identifying critical predictors such as retro-ocular pain, swollen eyelids, and age. This study not only establishes the superiority of a well-tuned ANN for this task but also demonstrates the value of broad model comparison and explainability in building reliable diagnostic tools for public health.