Spine Sound Classification: An Explainable Machine Learning Approach.

Low-cost, non-invasive screening tools for lumbar degenerative disease could complement imaging in resource-limited settings. This study presents a machine-learning framework to classify spine sounds recorded during lumbar motion and distinguish subjects with MRI-confirmed intervertebral disc and facet joint degeneration from asymptomatic controls. From each recording, we extract a 127-dimensional feature vector (MFCC statistics and temporal/spectral energy descriptors) and apply L1-norm-inducing kernel PCA (min and min-sign kernels) to obtain a compact, outlier-robust latent representation. Classification is performed using standard learners (SVM, Random Forest, Gradient Boosting, and KNN) under leave-one-patient-out (LOPO) cross-validation. The best configuration (min-kernel L1-kPCA with 20 components) achieves 93.3% accuracy with 93.0% sensitivity and 0.952 specificity (95% Wilson CI: 0.842-0.987) with an SVM classifier under rigorous LOPO cross-validation, outperforming the no-kPCA baseline (90.8% accuracy) and matching the neural network-based solution (93.3%). These results suggest that robust nonlinear feature compression can substantially improve spine sound classification and motivate external validation toward point-of-care screening.