Literature DB >> 28086200

An expert system for selecting wart treatment method.

Fahime Khozeimeh1, Roohallah Alizadehsani2, Mohamad Roshanzamir3, Abbas Khosravi4, Pouran Layegh5, Saeid Nahavandi6.   

Abstract

As benign tumors, warts are made through the mediation of Human Papillomavirus (HPV) and may grow on all parts of body, especially hands and feet. There are several treatment methods for this illness. However, none of them can heal all patients. Consequently, physicians are looking for more effective and customized treatments for each patient. They are endeavoring to discover which treatments have better impacts on a particular patient. The aim of this study is to identify the appropriate treatment for two common types of warts (plantar and common) and to predict the responses of two of the best methods (immunotherapy and cryotherapy) to the treatment. As an original work, the study was conducted on 180 patients, with plantar and common warts, who had referred to the dermatology clinic of Ghaem Hospital, Mashhad, Iran. In this study, 90 patients were treated by cryotherapy method with liquid nitrogen and 90 patients with immunotherapy method. The selection of the treatment method was made randomly. A fuzzy logic rule-based system was proposed and implemented to predict the responses to the treatment method. It was observed that the prediction accuracy of immunotherapy and cryotherapy methods was 83.33% and 80.7%, respectively. According to the results obtained, the benefits of this expert system are multifold: assisting physicians in selecting the best treatment method, saving time for patients, reducing the treatment cost, and improving the quality of treatment.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cryotherapy; Fuzzy rule-based system; Immunotherapy; Warts

Mesh:

Year:  2017        PMID: 28086200     DOI: 10.1016/j.compbiomed.2017.01.001

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  7 in total

1.  IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Authors:  Moloud Abdar; Vivi Nur Wijayaningrum; Sadiq Hussain; Roohallah Alizadehsani; Pawel Plawiak; U Rajendra Acharya; Vladimir Makarenkov
Journal:  J Med Syst       Date:  2019-06-07       Impact factor: 4.460

2.  Fractional Norms and Quasinorms Do Not Help to Overcome the Curse of Dimensionality.

Authors:  Evgeny M Mirkes; Jeza Allohibi; Alexander Gorban
Journal:  Entropy (Basel)       Date:  2020-09-30       Impact factor: 2.524

3.  Risk prediction of cardiovascular disease using machine learning classifiers.

Authors:  Madhumita Pal; Smita Parija; Ganapati Panda; Kuldeep Dhama; Ranjan K Mohapatra
Journal:  Open Med (Wars)       Date:  2022-06-17

Review 4.  Review of Machine Learning in Predicting Dermatological Outcomes.

Authors:  Amy X Du; Sepideh Emam; Robert Gniadecki
Journal:  Front Med (Lausanne)       Date:  2020-06-12

5.  Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems.

Authors:  Yamid Fabián Hernández-Julio; Martha Janeth Prieto-Guevara; Wilson Nieto-Bernal; Inés Meriño-Fuentes; Alexander Guerrero-Avendaño
Journal:  Diagnostics (Basel)       Date:  2019-05-09

Review 6.  Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey.

Authors:  Stefania Montani; Manuel Striani
Journal:  Yearb Med Inform       Date:  2019-08-16

7.  The Application of Machine Learning in Predicting Outcome of Cryotherapy and Immunotherapy for Wart Removal.

Authors:  Yashik Singh
Journal:  Ann Dermatol       Date:  2021-07-01       Impact factor: 1.444

  7 in total

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