Use of machine learning methods in diagnosis of carpal tunnel syndrome

Abstract

Carpal tunnel syndrome (CTS) is a common condition diagnosed using physical exams and electromyography (EMG) data. This study aimed to classify CTS severity using machine learning techniques. EMG data from 154 patients, including measurements of motor and sensory latency, velocity, and amplitude, were used to form a six-dimensional feature space. Classifiers such as DT, LDA, NB, SVM, k-NN, and ANN were applied, and the feature space was reduced using ANOVA, MRMR, Relieff, and PCA. The DT classifier with ANOVA feature selection showed the best performance for both full and reduced feature spaces.