The selection of wart treatment method based on Synthetic Minority Over-sampling Technique and Axiomatic Fuzzy Set theory

• Autorzy: Jia Wenjuan , Xia Hao , Jia Lijuan , Deng Yingjie , Liu Xiaodong

Identyfikatory

DOI

10.1016/j.bbe.2020.01.002

• Języki publikacji: EN

Abstrakty

EN

Wart disease is a kind of skin illness that is caused by Human Papillomavirus (HPV). Many medical studies are being carried out with the aid of machine learning and data mining techniques to find the most appropriate and effective treatment for a specific wart patient. However, the imbalanced distribution of medical data may lead to misclassification in this field. The purpose of this paper is to propose a algorithm to predict the response of the patients towards a specific treatment and choose an appropriate treatment method. In this paper, Synthetic Minority Over-sampling (SMOTE) method is adopted to deal with the unbalanced data and combined with Axiomatic Fuzzy Set (AFS) theory to predict whether patients can respond to treatment or not. Compared with other existing approaches, the proposed approach can provide descriptive information of the patients which can help to predict the response towards the treatment with an average prediction accuracy of 97.63% and 92.33% for cryotherapy and immunotherapy data, respectively. Furthermore, the ex-perimental results demonstrate that it can assist doctors in treatment, save medical resources and improve the quality of treatment.

Słowa kluczowe

EN

axiomatic fuzzy set theory; cryotherapy; immunotherapy; synthetic minority over-sampling; human papillomavirus

PL

teoria zbiorów rozmytych; krioterapia; immunoterapia; wirus brodawczaka ludzkiego

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 517--526
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr.

Twórcy

autor

Jia Wenjuan

• School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People's Republic of China

autor

Xia Hao

• School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People's Republic of China

autor

Jia Lijuan

• School of Bioengineering, Dalian University of Technology, Dalian, People's Republic of China

autor

Deng Yingjie

• School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People's Republic of China

autor

Liu Xiaodong

• School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).