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dc.contributor.authorSalmeron J.L.
dc.contributor.authorRahimi S.A.
dc.contributor.authorNavali A.M.
dc.contributor.authorSadeghpour A.
dc.date.accessioned2020-09-02T22:27:43Z
dc.date.available2020-09-02T22:27:43Z
dc.date.issued2017
dc.identifier10.1016/j.neucom.2016.09.113
dc.identifier.citation232, , 104-112
dc.identifier.issn09252312
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6168
dc.descriptionRheumatoid Arthritis (RA) is a chronic autoimmune disease that affect joints and muscles, and can result in noticeable disruption of joint structure and function. Early diagnosis of RA is very crucial in preventing disease's progression. However, it is a complicated task for General Practitioners (GPs) due to the wide spectrum of symptoms, and progressive changes in disease's direction over time. In order to assist physicians, and to minimize possible errors due to fatigued or less-experienced physicians, this study proposes an advanced decision support tool based on consultations with a group of experienced medical professionals (i.e. orthopedic surgeons and rheumatologists), and using a well-known soft computing method called Fuzzy Cognitive Maps (FCMs). First, a set of criteria for diagnosis of RA, based on previous studies and consultation with medical professionals have been selected. Then, Particle Swarm Optimization (PSO) and FCMs along with medical experts’ knowledge were used to model this problem and calculate the severity of the RA disease. Finally, a small-scale test has been conducted at Shohada University Hospital, Iran, for evaluating the accuracy of the proposed tool. Accuracy level of the tool reached to 90% and the results closely matched the medical professionals’ opinions. Considering obtained results in real practice, we believe that the proposed decision support tool can assist GPs in an accurate and timely diagnosis of patients with RA. Ultimately, the risk of wrong or late diagnosis will be diminished, and patients’ disease may be prevented from moving through the advanced stages. © 2016 Elsevier B.V.
dc.language.isoen
dc.publisherElsevier B.V.
dc.subjectDecision Support System
dc.subjectDiagnosis
dc.subjectFuzzy Cognitive Maps
dc.subjectMachine Learning
dc.subjectParticle Swarm Optimization
dc.subjectRheumatoid Arthritis disease
dc.subjectArtificial intelligence
dc.subjectCognitive systems
dc.subjectDecision support systems
dc.subjectDiseases
dc.subjectFuzzy rules
dc.subjectFuzzy systems
dc.subjectLearning systems
dc.subjectParticle swarm optimization (PSO)
dc.subjectSoft computing
dc.subjectAutoimmune disease
dc.subjectDecision support tools
dc.subjectFuzzy cognitive map
dc.subjectFuzzy cognitive maps (FCMs)
dc.subjectGeneral practitioners
dc.subjectMedical professionals
dc.subjectRheumatoid arthritis
dc.subjectSoft computing methods
dc.subjectDiagnosis
dc.subjectalgorithm
dc.subjectArticle
dc.subjectcalculation
dc.subjectclinical decision support system
dc.subjectclinical practice
dc.subjectcomputer model
dc.subjectconsultation
dc.subjectcontrolled study
dc.subjectdiagnostic accuracy
dc.subjectdiagnostic test
dc.subjectdiagnostic test accuracy study
dc.subjectdisease severity
dc.subjectfuzzy cognitive map
dc.subjectfuzzy system
dc.subjectgeneral practitioner
dc.subjecthuman
dc.subjectIran
dc.subjectjob experience
dc.subjectknowledge
dc.subjectmachine learning
dc.subjectmedical decision making
dc.subjectmedical specialist
dc.subjectorthopedic surgeon
dc.subjectparticle swarm optimization
dc.subjectrheumatoid arthritis
dc.subjectrheumatologist
dc.subjectsymptom
dc.titleMedical diagnosis of Rheumatoid Arthritis using data driven PSO–FCM with scarce datasets
dc.typeArticle


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