Adaptive neuro-fuzzy inference system for classification of ACL-ruptured knees using arthrometric data.
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.
Heydari
Zoya
Z
School of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran.
Farahmand
Farzam
F
Arabalibeik
Hossein
H
Parnianpour
Mohamad
M
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