Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study.

ABSTRACT: Degenerative cervical spondylosis, a chronic and progressive condition, has a considerable impact on global health. Spinal cord injury, a severe sequela of this disease, can result from this disease. Machine learning (ML) has emerged as a valuable tool for medical data analysis, effectively predicting disease outcomes. A multicenter study involving retrospective analysis of data from 737 patients diagnosed with cervical spondylosis was performed. On the basis of clinical data obtained from three hospitals, a predictive model was developed and demonstrated using multiple ML algorithms. In accordance with the exclusion criteria, a training set consisting of 385 samples, a test set of 129 samples, and an external validation set of 149 samples were acquired. Through univariate analysis and LAS

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC12559745 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature