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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics
Arrizal Kusuma, Moch Farrel
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Addressing Imbalance Data Label Distribution in Dengue Severity Classification with Hybrid Machine Learning Approach Arrizal Kusuma, Moch Farrel; Anggraeni, Wiwik
ILKOMNIKA Vol 6 No 3 (2024): Volume 6, Nomor 3, Desember 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v6i3.649

Abstract

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.
Co-Authors Wiwik Anggraeni