A stacked ensemble approach for symptom-based monkeypox diagnosis

Abstract

The recent monkeypox outbreak has raised global health concerns. Caused by a virus, it is characterized by symptoms such as skin lesions. Early detection is critical for treatment and controlling its spread. This study uses advanced machine learning and deep learning techniques, including Tab Transformer, Long Short-Term Memory, XGBoost, LightGBM, and a Stacking Classifier, to predict the presence of the virus based on patient symptoms. The performance of these models is evaluated using accuracy, precision, recall, and F1-score metrics. The experiments reveal that the Stacking Classifier significantly outperforms the other models, achieving an accuracy of 87.29 %, precision of 86.12 %, recall of 87.47 %, and an F1 score of 87.89 %. Additionally, applying Conditional Tabular GAN to generate synthetic data helps address data imbalance issues, further improving model robustness. These results highlight the proposed approach's potential for timely, accurate monkeypox detection, aiding in effective disease management and control.