An expert system design to diagnose cancer by using a new method reduced rule base

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dc.authoridBasciftci, Fatih -- 0000-0003-1679-7416; AVUCLU, Emre -- 0000-0002-1622-9059
dc.contributor.authorBasçiftçi, Fatih
dc.contributor.authorAvuçlu, Emre
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:15:16Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:15:16Z
dc.date.issued2018
dc.departmentAksaray Teknik Bilimler Meslek Yüksekokulu
dc.description.abstractBackground and objectives: A Medical Expert System (MES) was developed which uses Reduced Rule Base to diagnose cancer risk according to the symptoms in an individual. A total of 13 symptoms were used. With the new MES, the reduced rules are controlled instead of all possibilities (2(13) = 8192 different possibilities occur). By controlling reduced rules, results are found more quickly. The method of two-level simplification of Boolean functions was used to obtain Reduced Rule Base. Thanks to the developed application with the number of dynamic inputs and outputs on different platforms, anyone can easily test their own cancer easily. Methods: More accurate results were obtained considering all the possibilities related to cancer. Thirteen different risk factors were determined to determine the type of cancer. The truth table produced in our study has 13 inputs and 4 outputs. The Boolean Function Minimization method is used to obtain less situations by simplifying logical functions. Diagnosis of cancer quickly thanks to control of the simplified 4 output functions. Results: Diagnosis made with the 4 output values obtained using Reduced Rule Base was found to be quicker than diagnosis made by screening all 2(13) = 8192 possibilities. With the improved MES, more probabilities were added to the process and more accurate diagnostic results were obtained. As a result of the simplification process in breast and renal cancer diagnosis 100% diagnosis speed gain, in cervical cancer and lung cancer diagnosis rate gain of 99% was obtained. Conclusions: With Boolean function minimization, less number of rules is evaluated instead of evaluating a large number of rules. Reducing the number of rules allows the designed system to work more efficiently and to save time, and facilitates to transfer the rules to the designed Expert systems. Interfaces were developed in different software platforms to enable users to test the accuracy of the application. Any one is able to diagnose the cancer itself using determinative risk factors. Thereby likely to beat the cancer with early diagnosis. (C) 2018 Elsevier B.V. All rights reserved.
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research Projects
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.
dc.identifier.doi10.1016/j.cmpb.2018.01.020
dc.identifier.endpage120en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid29477419
dc.identifier.scopusqualityQ1
dc.identifier.startpage113en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2018.01.020
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4337
dc.identifier.volume157en_US
dc.identifier.wosWOS:000425897400011
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofComputer Methods and Programs In Biomedicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectCancer Symptoms and Types
dc.subjectMinimization Method
dc.subjectMobile Programming
dc.subjectExpert System
dc.titleAn expert system design to diagnose cancer by using a new method reduced rule base
dc.typeArticle

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