SyMPox: An Automated Monkeypox Detection System Based on Symptoms Using XGBoost
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Abstract (arXiv)
Monkeypox is a zoonotic disease. About 87000 cases of monkeypox were confirmed by the World Health Organization until 10th June 2023. The most prevalent methods for identifying this disease are image-based recognition techniques. Still, they are not too fast and could only be available to a few individuals. This study presents an independent application named SyMPox, developed to diagnose Monkeypox cases based on symptoms. SyMPox utilizes the robust XGBoost algorithm to analyze symptom patterns and provide accurate assessments. Developed using the Gradio framework, SyMPox offers a user-friendly platform for individuals to assess their symptoms and obtain reliable Monkeypox diagnoses.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 2 Oct 2023.
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6 of 30 references.
References · 6
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 176
- 2021Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostcited by 8
- 2021A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Imagescited by 6
- 2022Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Methodcited by 3
- 2023PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Diseasecited by 1
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