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

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.

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