Multidimensional machine learning approach for classifying patients with neck pain based on movement control test

Abstract

Background

Patients with neck pain (NP) commonly display impairments in cervical movement control. Improvements in technology used to assess movement control allow extraction of various parameters that could enable more precise classification, but this has not yet been investigated.

Objective

This study aimed to assess validity (classification performance of different machine learning models) of different parameters and difficulty levels of the Butterfly cervical movement control test.

Methods

Sixty-five patients with NP and fifty asymptomatic controls performed the Butterfly test. Random forest, support vector machines and logistic regression classification methods were used to build models based on individual or multiple parameters across different difficulty levels. The area under the curve (AUC), classification accuracy, F1 score, precision, recall, specificity, information gain and Gini index were reported.

Results

Models using amplitude accuracy, time-on-target or undershoot achieved the highest classification performance. However, models combining directional accuracy or all parameters of the Butterfly test at all difficulty levels yielded the most balanced probability of classification.

Conclusion

Results of our study suggest that either directional accuracy or all parameters of the Butterfly test (calculated for all difficulty levels) should be used for classifying patients with NP to provide the most clinically valid tool.