Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms.

ABSTRACT:

Background

Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM.

Methods

This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating

SUBMITTER: Park D 

PROVIDER: S-EPMC9485547 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature