Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 1 2 2014 06 30 103 110 EN Amjad Hashemi Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran. Hossein Arabalibeik Research Centre of Biomedical Technology and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran. Farzam Farahmand School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran. 2015 10 13 2015 10 13 Many people suffer from the anterior cruciate ligament (ACL) injury, which can lead to knee instability associated with damage to other knee structures Purpose: In this study we present a classification method based on aggregation operators, using Adaptive Network-based Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) neural network to differentiate between arthrometric data of normal and ACL-ruptured knees. Methods: The data involves 132 samples consisting of 59 patients with injured knee and73 normal subjects. ANFIS hybrid training algorithm is implemented using Fuzzy C-Means (FCM) and subtractive data clustering. The Levenberg–Marquardt (LM) training algorithm is used for MLP neural network. The results of ANFIS and MLP are then combined using aggregation operators. Results: The best accuracy (96%) is obtained by applying Choquet integral to the outputs of ANFIS classifier with the antecedent parameters selected using FCM algorithm. Conclusion: The experimental results show that aggregation operators enhance the outcomes of ANFIS and MLP classifiers in discriminating between ACL raptured knees and normal subjects. https://fbt.tums.ac.ir/index.php/fbt/article/view/29 https://fbt.tums.ac.ir/index.php/fbt/article/download/29/28