Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction

Abstract This study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable of anticipating modifications in cervical lordotic angle (CLA), pain and disability of 570 patients treated between 2014 and 2020. Linear regression models used pre-treatment variables of age, body mass index, CLA, anterior head translation, disability index, pain score, treatment frequency, duration and compliance. These models used the sci-kit-learn machine learning library within Python for implementing linear regression algorithms. The linear regression models demonstrated high precision and accuracy, and effectively explained 30–55% of the variability in post-treatment outcomes, the highest for the CLA. This pioneering study integrates machine learning into spinal rehabilitation. The developed models offer valuable information to customize interventions, set realistic expectations, and optimize treatment strategies based on individual patient characteristics as treated conservatively with rehabilitation programs using CET as part of multimodal care.