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Adaptive neuro-fuzzy inference system for classification of ACL-ruptured knees using arthrometric data

Heydari, Z ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1007/s10439-008-9532-x
  3. Publisher: 2008
  4. Abstract:
  5. 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. © 2008 Biomedical Engineering Society
  6. Keywords:
  7. Classification (of information) ; Classifiers ; Clustering algorithms ; Fuzzy inference ; Health ; Learning systems ; Adaptive neuro-fuzzy inference system ; Adaptive-network-based fuzzy inference systems ; ANFIS ; ANFIS classifier ; Classification ; Cross validations ; Data clustering ; Data points ; Force-displacement curves ; Hybrid algorithm ; Hypermobility ; Knee arthrometer ; New approaches ; Sensitivity and specificity ; Superior performance ; Fuzzy systems ; Algorithm ; Arthrometry ; Differential diagnosis ; Human ; Injury ; Knee injury ; Methodology ; Pathophysiology ; Adult ; Algorithms ; Anterior cruciate ligament ; Diagnosis ; Female ; Humans ; Knee injuries ; Male ; Differential
  8. Source: Annals of Biomedical Engineering ; Volume 36, Issue 9 , 9 July , 2008 , Pages 1449-1457 ; 00906964 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10439-008-9532-x