{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:06:32Z","timestamp":1777705592330,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:p>Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient\u2019s medical history. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, and the results were compared. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient\u2019s medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. The DSS is web-based and publicly accessible. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. To examine the effect of the patient\u2019s history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient\u2019s history. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR \/ EULAR RA classification criteria. The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. In the study of machine learning techniques, the highest success rate was achieved with the LR model. While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient\u2019s history. In addition to the high success rate, the results show that the patient\u2019s history is important in diagnosing RA disease.<\/jats:p>","DOI":"10.3233\/jifs-221582","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T12:03:58Z","timestamp":1666353838000},"page":"5543-5557","source":"Crossref","is-referenced-by-count":5,"title":["Diagnosing rheumatoid arthritis disease using fuzzy expert system and machine learning techniques"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7399-5999","authenticated-orcid":false,"given":"Fatih","family":"Tarakci","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Institute of Sciences, Selcuk University, Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-1040","authenticated-orcid":false,"given":"Ilker Ali","family":"Ozkan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Selcuk University, Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4277-3880","authenticated-orcid":false,"given":"Sema","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Selcuk University School of Medicine, Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-9770","authenticated-orcid":false,"given":"Dilek","family":"Tezcan","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Selcuk University School of Medicine, Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-221582_ref4","doi-asserted-by":"crossref","first-page":"101438,","DOI":"10.1016\/j.berh.2019.101438","article-title":"How to Investigate: Pre-clinical Rheumatoid Arthritis, (in eng)","volume":"33","author":"Martins","year":"2019","journal-title":"Best Pract Res Clin Rheumatol"},{"issue":"11","key":"10.3233\/JIFS-221582_ref5","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/j.amjmed.2007.04.005","article-title":"Rheumatoid Arthritis: Diagnosis and Management, (in eng)","volume":"120","author":"Majithia","year":"2007","journal-title":"Am J Med"},{"issue":"6055","key":"10.3233\/JIFS-221582_ref6","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1136\/bmj.1.6055.195","article-title":"Rheumatoid Arthritis: Relation of Serum C-reactive Protein and Erythrocyte Sedimentation Rates to Radiographic Changes, (in eng)","volume":"1","author":"Amos","year":"1977","journal-title":"Br Med J"},{"issue":"9","key":"10.3233\/JIFS-221582_ref8","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1002\/art.27584","article-title":"Rheumatoid Arthritis Classification Criteria: An American College of Rheumatology\/European League Against Rheumatism Collaborative Initiative, (in eng)","volume":"62","author":"Aletaha","year":"2010","journal-title":"Arthritis Rheum"},{"issue":"4","key":"10.3233\/JIFS-221582_ref9","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1016\/j.eswa.2014.10.026","article-title":"Expert System for Medicine Diagnosis Using Software Agents","volume":"42","author":"Arsene","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-221582_ref10","first-page":"155","article-title":"Artificial Intelligence And Machine Learning Applications in Big Data Analysis,niversitesi Sosyal Bilimler Enstit\u00fcs\u00fc Dergisi pp. \u2013","volume":"9","author":"Atalay","year":"2017","journal-title":"Mehmet Akif Ersoy"},{"issue":"7","key":"10.3233\/JIFS-221582_ref11","first-page":"218","article-title":"Medical Expert Systems Survey","volume":"1","author":"Abu-Nasser","year":"2017","journal-title":"International Journal of Engineering and Information Systems (IJEAIS)"},{"issue":"3","key":"10.3233\/JIFS-221582_ref12","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1007\/s10916-010-9606-9","article-title":"Diagnosis of Arthritis Through Fuzzy Inference System, (in eng),","volume":"36","author":"Singh","year":"2012","journal-title":"J Med Syst"},{"key":"10.3233\/JIFS-221582_ref13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1260\/1748-3018.9.3.265","article-title":"Development of Decision Support System for the Diagnosis of Arthritis Pain for Rheumatic Fever Patients: Based on the Fuzzy Approach","volume":"9","author":"Pandey","year":"2015","journal-title":"Journal of Algorithms & Computational Technology"},{"key":"10.3233\/JIFS-221582_ref15","doi-asserted-by":"crossref","unstructured":"Siddiqui S.Y. , Hussnain S.A. , Siddiqui A.H. , Ghufran R. 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