Addressing Imbalance Data Label Distribution in Dengue Severity Classification with Hybrid Machine Learning Approach - Arrizal Kusuma, Moch Farrel; Anggraeni, Wiwik (ILKOMNIKA, 2024)
Early detection of disease is essential for ensuring proper diagnosis and treatment, which helps to reduce complications and limit the spread of cases. Dengue fever remains a major public health issue in Indonesia, with some regions reporting case fatality rates above 1%. This study aims to develop a robust classification model to predict the severity of dengue fever based on laboratory data, addressing the issue of imbalanced label distribution. To achieve this, we applied a combination of oversampling, machine learning, and optimization techniques. Specifically, we utilized the SMOTE-ENN method to enhance the representation of minority classes and applied XGBoost for multi-class classification. Additionally, Particle Swarm Optimization (PSO) was used to fine-tune the model’s hyperparameters. After testing 15 experimental scenarios, the hybrid SMOTE-ENN-XGBoost-PSO approach delivered the best performance, achieving an accuracy of 95% and an F1-score of 94%. These results demonstrate that the proposed approach effectively handles data imbalance and improves predictive accuracy. This research serves as a foundation for future work in applying advanced algorithms and Federated Learning to improve multi-class classification in healthcare.