title: Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
abstract: Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were evaluated for their conditions in other patient groups.
authors.affiliation: Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey.
authors.forename: Evren
authors.initials: E
authors.lastname: Arslan
authors.affiliation: Department of Physical Medicine and Rehabilitation, Egirdir Bone & Joint Disease Treatment and Rehabilitation Hospital, Isparta, Turkey.
authors.forename: Sedat
authors.initials: S
authors.lastname: Yildiz
authors.affiliation: Department of Electrical and Electronics Engineering, Faculty of Engineering, Akdeniz University, Antalya, Turkey. yalbayrak@akdeniz.edu.tr.|Sakarya University, Sakarya, Turkey. yalbayrak@akdeniz.edu.tr.
authors.forename: Yalcin
authors.initials: Y
authors.lastname: Albayrak
authors.affiliation: Sakarya University, Sakarya, Turkey.|Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey.
authors.forename: Etem
authors.initials: E
authors.lastname: Koklukaya
mesh_terms: D000328:Adult; D003710:Demography; D005260:Female; D005356:Fibromyalgia / Q000145:classification* / Q000175:diagnosis; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D010146:Pain / Q000150:complications; D010147:Pain Measurement; D013577:Syndrome
keywords: ACR criteria; Clinical diagnosis; Fibromyalgia severity; Fibromyalgia syndrome; Fuzzy logic