title: Adaptive neuro-fuzzy inference system for classification of ACL-ruptured knees using arthrometric data.
abstract: A new approach, based on Adaptive-Network-based Fuzzy Inference System (ANFIS), is presented for the classification of arthrometric data of normal/ACL-ruptured knees, considering the insufficiency of existing criteria. An ANFIS classifier was developed and tested on a total of 4800 arthrometric data points collected from 40 normal and 40 injured subjects. The system consisted of 5 layers and 8 rules, based on the results of subtractive data clustering, and trained using the hybrid algorithm method. The performance of the system was evaluated in four runs, in the framework of a 4-fold cross validation algorithm. The results indicated a definite correct diagnosis for typical injured and normal cases. Except for two, all cases with marginally distinct force-displacement curves were also diagnosed correctly. The overall sensitivity and specificity of the system in four runs were 95.5% and 100%, respectively. The superior performance of the ANFIS classifier over previously suggested criteria highlights its capability when dealing with marginal arthrometric data of knees with partially disrupted ACL or hypermobility syndrome.
authors.affiliation: School of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran.
authors.forename: Zoya
authors.initials: Z
authors.lastname: Heydari
authors.forename: Farzam
authors.initials: F
authors.lastname: Farahmand
authors.forename: Hossein
authors.initials: H
authors.lastname: Arabalibeik
authors.forename: Mohamad
authors.initials: M
authors.lastname: Parnianpour
mesh_terms: D000328:Adult; D000465:Algorithms; D016118:Anterior Cruciate Ligament / Q000503:physiopathology; D000070598:Anterior Cruciate Ligament Injuries; D053587:Arthrometry, Articular / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007718:Knee Injuries / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D008297:Male; D012680:Sensitivity and Specificity