Utilizing Machine Learning To Predict Post-treatment Outcomes In Chronic Non-specific Neck Pain Patients Undergoing Cervical Extension Traction

This article discusses the use of machine learning models in predicting patient outcomes for a multimodal rehabilitation program using cervical extension traction (CET) to improve cervical spine alignment in patients with chronic non-specific neck pain. Factors such as pre-treatment ARA, frequency and duration of CET, age, and compliance were found to significantly influence outcomes. This analysis provides insight into the effectiveness of CET and the potential for machine learning in healthcare.