10.6084/m9.figshare.7565888.v1
Sümeyye Çelik
Sümeyye
Çelik
Melike Şişeci Çeşmeli
Melike Şişeci
Çeşmeli
İhsan Pençe
İhsan
Pençe
Adnan Kalkan
Adnan
Kalkan
Siğil Tedavisinde Kullanılan Immunotherapy Yönteminin Uygunluğunun Bayes Yöntemi ile Tespiti
IMISC
2019
imisc
Wart treatment
Bayes net
Immunotherapy
Data mining
Business Information Systems
2019-01-09 19:00:18
Journal contribution
https://imisc.figshare.com/articles/journal_contribution/Sig_il_Tedavisinde_Kullan_lan_Immunotherapy_Yo_nteminin_Uygunlug_unun_Bayes_Yo_ntemi_ile_Tespiti/7565888
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<p><b>Title
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<p>Determination of the Suitability of Immunotherapy Method Used in the Wart Treatment Using Bayes Method
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<p><b>Abstract
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<p>Wart disease is a common occurrence in medicine. According to their characteristics, treatment with several different
methods is possible. When the treatment method to be applied is selected, the method which is deemed most appropriate is
selected according to the features accepted in the literature. In this study, according to the characteristics determined in the
literature, a preliminary evaluation was made with the data mining methods on the immunotherapy method applied to the
patient and the evaluation success rate was increased. This will help the doctor to decide whether to choose the
immunotherapy method when choosing a treatment method. Various methods have been tested on data sets in order to
increase the success rate. According to the observed results, the highest success rate was 85.55% in bayes net classification.
The data set used in the study is the results of a scientific research conducted in the dermatology clinic of the Iranian Ghaem
Hospital, published in the UCI machine learning repository.The Bayesian net algorithm was implemented using the Waikato
Environment for Knowledge Analysis (WEKA).
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<p><b>Editor:</b> H. Kemal İlter, Ankara Yıldırım Beyazıt University, Turkey
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<p><b>Received: </b>August 19, 2018, <b>Accepted: </b>October 18, 2018, <b>Published: </b>November 10, 2018<br><b>
Copyright:</b> © 2018 IMISC Çelik et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited. </p>
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