Aggregation operators enhance the classification of ACL-ruptured knees using arthrometric data

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 and 73 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.