ArticleDiagnostics (Basel, Switzerland)2023
Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Using Artificial Intelligence to Develop Clinical Decision Support Systems-The Evolving Road of Personalized Oncologic Therapy.Diagnostics (Basel, Switzerland) · 2025Article
- Modeling energy consumption indexes of an industrial cement ball mill for sustainable production.Scientific reports · 2025Article
- Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.BMC infectious diseases · 2025Article
- An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.Scientific reports · 2025Article
- An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis.Scientific reports · 2025Article
- Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system.Scientific reports · 2025Article
- Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.Molecules (Basel, Switzerland) · 2024Article
- Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.Tomography (Ann Arbor, Mich.) · 2024Article
- Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome.PeerJ. Computer science · 2024Article
- A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
The monkeypox virus poses a novel public health risk that might quickly escalate into a worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, finding tumor cells, and finding COVID-19 patients. In this study, we have created a dataset based on the data both collected and published by Global Health and used by the World Health Organization (WHO). Being entirely textual, this dataset shows the relationship between the symptoms and the monkeypox disease. The data have been analyzed, using gradient boosting methods such as Extreme Gradient Boosting (XGBoost), CatBoost, and LightGBM along with other standard machine learning methods such as Support Vector Machine (SVM) and Random Forest. All these methods have been compared. The research aims to provide an ML model based on symptoms for the diagnosis of monkeypox. Previous studies have only examined disease diagnosis using images. The best performance has belonged to XGBoost, with an accuracy of 1.0 in reviews. To check the model's flexibility, k-fold cross-validation is used, reaching an average accuracy of 0.9 in 5 different splits of the test set. In addition, Shapley Additive Explanations (SHAP) helps in examining and explaining the output of the XGBoost model.
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