1. An expert system design to diagnose cancer by using a new method reduced rule base
- Author
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Fatih Başçiftçi, Emre Avuçlu, [Basciftci, Fatih] Selcuk Univ, Technol Fac, Dept Comp Engn, TR-42003 Selcuklu, Konya, Turkey -- [Avuclu, Emre] Aksaray Univ, Dept Comp Technol & Comp Programming, Aksaray, Turkey, Basciftci, Fatih -- 0000-0003-1679-7416, AVUCLU, Emre -- 0000-0002-1622-9059, and Aksaray Teknik Bilimler Meslek Yüksekokulu
- Subjects
Lung Neoplasms ,Computer science ,0206 medical engineering ,Uterine Cervical Neoplasms ,Cancer symptoms and types ,Health Informatics ,Expert Systems ,02 engineering and technology ,computer.software_genre ,Search engine ,Software ,Mobile programming ,Risk Factors ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Boolean function ,Early Detection of Cancer ,Expert system ,business.industry ,Truth table ,Reproducibility of Results ,Minimization method ,020601 biomedical engineering ,Kidney Neoplasms ,Computer Science Applications ,020201 artificial intelligence & image processing ,Female ,Data mining ,business ,computer ,Algorithms - Abstract
WOS: 000425897400011, PubMed: 29477419, Background 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., Coordinatorship of Selcuk University's Scientific Research Projects, This work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.
- Published
- 2018