Machine learning based identification and classification of disorders in human knee joint – computational approach.

Earlier identification of knee joint pathology helps the therapist to provide the appropriate clinical procedures to control the deteriorating process of arthritis. Beyond usual medical investigations, computational techniques have been used for the diagnosis of knee joint disorder. Among different methodologies, vibroarthrographic technique is employed to identify knee joint disorder. Machine learning contains number of classification methods for the given data. A novel technique called greedy sequential backward feature selection-based radial kernelized least square support vector classification (GSBFS-RKLSSVC) is introduced for accurate detection of knee joint pathology with minimum time. The proposed GSBFS-RKLSSVC technique consists of three processes, namely feature selection, feature evaluation, and classification. Initially, number of VAG signal images is taken from the dataset for detection of knee joint disorder. The relevant feature is selected through the greedy mutual informative regressed sequential backward selection algorithm to reduce an initial dimensional feature space into a low-dimensional feature subspace. Following this, the dichotomous logit regression is applied to select the best features and discard others. Therefore, the feature selection process of the proposed GSBFS-RKLSSVC minimizes the time consumption of the knee joint pathology detection. Once the signal features are extracted, RKLSSVC is applied to detect the normal and abnormal VAG signal. Decision boundary is utilized by the classifier to categorize the samples based on the similarity between the training features and testing features. As a result, the accurate classification is obtained with a minimum error rate. The observed result indicates that GSBFS-RKLSSVC achieves higher accuracy, sensitivity, and specificity and reduces time than the conventional methods. [ABSTRACT FROM AUTHOR]