Efficient Subject-Independent Detection of Anterior Cruciate Ligament Deficiency Based on Marine Predator Algorithm and Support Vector Machine

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 10; pp. 4936 - 4947
• Main Authors: Wang, Gengyuan, Zeng, Xiaolong, Lai, Guanquan, Zhong, Guoqing, Ma, Ke, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anterior cruciate ligament; Anterior cruciate ligament (ACL) deficiency; Classification; Classification algorithms; Feature extraction; feature selection and classification; Frequency domain analysis; Gait; gait kinematic data; Health risks; Heuristic methods; Kinematics; Knee; Ligaments; marine predator algorithm (MPA); Medical diagnosis; Medical diagnostic imaging; Nonlinear systems; Optimization; Predators; Sensitivity; support vector machine (SVM); Support vector machines
• DOI: 10.1109/JBHI.2022.3152846
• ISSN: 2168-2194; 2168-2208

Anterior cruciate ligament (ACL) deficiency not only reduces knee stability, but also increases the risk of more disease and impairs daily life, thus requiring efficient detection of ACL deficiency. To build an efficient subject-independent ACL deficiency detection model, this study proposes a new method called SVM-MPA that fuses marine predator algorithm (MPA) and support vector machine (SVM) for simultaneous feature selection, hyperparameter optimization and classification. 35ACL-deficient (ACLD) and 35 ACL-intact (ACLI) participants were recruited to collect 6-degree-of-freedom knee kinematic data. Then, 216-dimensional multi-domain features covering time domain, frequency domain, time-frequency domain and nonlinearity were extracted. The error rate of SVM classification based on 5-fold cross-validation was used to construct the fitness of MPA, and MPA served to select features and optimize two hyperparameters for SVM. The majority voting strategy-based post-processing was introduced to convert the gait cycle-level to knee-level ACL deficiency detection. Comparing with 7 well-known meta-heuristic algorithms and running all 20 times, the best average gait cycle-level ACL deficiency detection performance (sensitivity: 96.78±0.4.84%, specificity: 99.43±5.70%, and accuracy: 98.48±1.70%) was obtained using the proposed method. With post-processing, this study improved the best (final) detection performance (sensitivity: 97.78±4.97%, specificity: 100±0.00%, and accuracy: 99.13±1.94%). These results demonstrate the feasibility and effectiveness of the proposed method and shows that an efficient subject-independent ACL deficiency detection model can be constructed using the proposed method, which makes it possible to provide a non-invasive, objective and accurate preoperative auxiliary detection method for diagnosing ACL deficiency clinically.