Comparing Random Forest and Gradient Boosting for Monkeypox Diagnosis

Early and accurate diagnosis of Monkeypox is essential to limit transmission and support effective treatment. This study aims to compare the performance of Random Forest and Gradient Boosting models for classifying Monkeypox cases using clinical symptom data. A synthetic dataset from Kaggle containing 25,000 records with 11 symptom-based features was used to evaluate both models under imbalanced and SMOTE-balanced conditions using stratified 5-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curves, and area under the curve (AUC). The experimental results indicate that both models achieve high recall values on imbalanced data, with Gradient Boosting slightly outperforming Random Forest in discriminative performance (AUC 0.6869 vs. 0.6839). While the application of SMOTE improves precision, it reduces recall and provides only marginal improvements in AUC, indicating a trade-off between sensitivity and precision in symptom-based classification. These findings demonstrate the potential of ensemble learning models for symptom-based Monkeypox classification in synthetic tabular datasets. However, further validation using real-world clinical data is necessary before practical diagnostic deployment.

@article{Rizki2026,
  title     = {Comparing Random Forest and Gradient Boosting for Monkeypox Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Fahlul Rizki and Widowati and Catur Edi Widodo},
  doi       = {10.14569/IJACSA.2026.0170309},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170309}
}