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 has 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 at providing an ML model based on
symptoms to diagnose 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 split of test set. In addition, Shapley
Additive Explanations (SHAP) helps examining and explaining the output
of the XGBoost model.