Dengue Fever Detection Using Swarm Intelligence and XGBoost Classifier: An Interpretable Approach with SHAP and DiCE

Dengue fever is a mosquito-borne viral disease that annually affects 100–400 million people worldwide. Early detection of dengue enables easy treatment planning and helps reduce mortality rates. This study proposes three Swarm-based Metaheuristic Algorithms, Golden Jackal Optimization, Fox Optimizer, and Sea Lion Optimization, for feature selection and hyperparameter tuning, and an Extreme Gradient Boost classifier to forecast dengue fever using the Predictive Clinical Dengue dataset. Several existing models have been proposed for dengue fever classification, with some achieving high predictive performance. However, most of these studies have overlooked the importance of feature reduction, which is crucial to building efficient and interpretable models. Furthermore, prior research has lacked in-depth analysis of model behavior, particularly regarding the underlying causes of misclassification. Addressing these limitations, this study achieved a 10-fold cross-validation mean accuracy of 99.89%, an F-score of 99.92%, a precision of 99.84%, and a perfect recall of 100% by using only two features: WBC Count and Platelet Count. Notably, FOX-XGBoost and SLO-XGBoost achieved the same performance while utilizing only four and three features, respectively, demonstrating the effectiveness of feature reduction without compromising accuracy. Among these, GJO-XGBoost demonstrated the most efficient feature utilization while maintaining superior performance, emphasizing its potential for practical deployment in dengue fever diagnosis. SHAP analysis identified WBC Count as the most influential feature driving model predictions. Furthermore, DiCE explanations support this finding by showing that lower WBC Counts are associated with dengue-positive cases, whereas higher WBC Counts are indicative of dengue-negative individuals. SHAP interpreted the reasons behind misclassifications, while DiCE provided a correction mechanism by suggesting the minimal changes needed to convert incorrect predictions into correct ones.