Machine learning-based detection of cervical spondylotic myelopathy using multiple gait parameters

Cervical spondylotic myelopathy (CSM) is the main cause of adult spinal cord dysfunction, mostly appearing in middle-aged and elderly patients. Currently, the diagnosis of this condition depends mainly on the available imaging tools such as X-ray, computed tomography and magnetic resonance imaging (MRI), of which MRI is the gold standard for clinical diagnosis. However, MRI data cannot clearly demonstrate the dynamic characteristics of CSM, and the overall process is far from cost-efficient. Therefore, this study proposes a new method using multiple gait parameters and shallow classifiers to dynamically detect the occurrence of CSM. In the present study, 45 patients with CSM and 45 age-matched asymptomatic healthy controls (HCs) were recruited, and a three-dimensional (3D) motion capture system was utilized to capture the locomotion data. Furthermore, 63 spatiotemporal, kinematic, and nonlinear parameters were extracted, including lower limb joint angles in the sagittal, coronal, and transverse planes. Then, the Shapley Additive exPlanations (SHAP) value was utilized for feature selection and reduction of the dimensionality of features, and five traditional shallow classifiers, including support vector machine (SVM), logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF), were used to classify gait patterns between CSM patients and HCs. On the basis of the 10-fold cross-validation method, the highest average accuracy was achieved by SVM (95.56%). Our results demonstrated that the proposed method could effectively detect CSM and thus serve as an automated auxiliary tool for the clinical diagnosis of CSM.