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.

在运动控制过程中中断感觉运动整合的周围神经病变将导致日常生活中手部和上肢运动功能不足。但是，从未在可用的诊断和临床评估中考虑过行为生物力学和运动功能。先前研究行为生物力学以描绘特定的周围神经病变及其严重程度的先前研究表明，某些生物力学参数有可能被识别为从早期阶段检测神经病变的生物标记物。然而，由行为生物力学参数形成的数据集通常具有高维，小样本量和高冗余的特点，这给我们在患者和健康对照之间进行二元分类带来了挑战。我们提出了一个两阶段的机器学习协议，该协议使用随机森林（RF）进行早期变量筛选，使用（K）PCA-SVM系统进行生物标记的预测和最终鉴定。我们将拟议的协议应用于腕管综合症（CTS）的示例应用，其预测准确率达到90.3％，从700个初始输入变量中识别出6个生物标志物变量。这些有希望的结果为将来临床测试设计的指南和方向向新型诊断优化提供了范式转变。我们将拟议的协议应用于腕管综合症（CTS）的示例应用，其预测准确率达到90.3％，从700个初始输入变量中识别出6个生物标志物变量。这些有希望的结果为将来临床测试设计的指南和方向向新型诊断优化提供了范式转变。我们将拟议的协议应用于腕管综合症（CTS）的示例应用，其预测准确率达到90.3％，从700个初始输入变量中识别出6个生物标志物变量。这些有希望的结果为将来临床测试设计的指南和方向向新型诊断优化提供了范式转变。