A two-phase iterative machine learning method in identifying mechanical biomarkers of peripheral neuropathy

Abstract Peripheral neuropathy that interrupts sensorimotor integration in the motor control process will lead to hand and upper limb motor function deficits in daily life. However, behavioral biomechanics and motor functions have never been considered in available diagnoses and clinical evaluations. Previous studies that investigate the behavioral biomechanics to delineate a specific peripheral neuropathy and its severity have shown evidences that certain biomechanical parameters have the potential to be identified as biomarkers for the detection of the neuropathy from an early stage. Nevertheless, datasets formed by behavioral biomechanical parameters are often characterized by the high dimensionality, the small sample size, and the high redundancy, which brings us challenges for making binary classification between patients and healthy controls. We propose a two-phase machine learning protocol using Random Forests (RFs) for the early variable screening and the (K)PCA-SVM system for the prediction and the final identification of biomarkers. We apply the proposed protocol to an example application of Carpal Tunnel Syndrome (CTS) and its prediction accuracy reaches 90.3% with 6 biomarker variables identified from 700 initial input variables. These promising results provide a paradigm shift of guidelines and directions of clinical test designs toward novel diagnostic optimization in the future.