Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Computing and Communications

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https://doi.org/10.3390/diagnostics13142391
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Keywords

machine learning, monkeypox, MPXV, SHAP, XGBoost

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods. / Farzipour, Alireza; Elmi, Roya; Nasiri, Hamid.
In: Diagnostics, Vol. 13, No. 14, 2391, 17.07.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{8b0e9fa0043143a5822ed90445b0e47b,

title = "Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods",

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{\textquoteright}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.",

keywords = "machine learning, monkeypox, MPXV, SHAP, XGBoost",

author = "Alireza Farzipour and Roya Elmi and Hamid Nasiri",

note = "Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = jul,

day = "17",

doi = "10.3390/diagnostics13142391",

language = "English",

volume = "13",

journal = "Diagnostics",

issn = "2075-4418",

publisher = "MDPI",

number = "14",

}

RIS

TY - JOUR

T1 - Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

AU - Farzipour, Alireza

AU - Elmi, Roya

AU - Nasiri, Hamid

PY - 2023/7/17

Y1 - 2023/7/17

N2 - 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.

AB - 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.

KW - machine learning

KW - monkeypox

KW - MPXV

KW - SHAP

KW - XGBoost

U2 - 10.3390/diagnostics13142391

DO - 10.3390/diagnostics13142391

M3 - Journal article

C2 - 37510135

AN - SCOPUS:85169106393

VL - 13

JO - Diagnostics

JF - Diagnostics

SN - 2075-4418

IS - 14

M1 - 2391

ER -

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