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 characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification.

Results: In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors.

Conclusion: ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.

Keywords: cervical spondylosis; decision making; machine learning; myelopathy; therapeutic options.

Figure 1

Schematic architecture of the study design. ASA, anterior surgical approaches; DCM, degenerative cervical myelopathy; MC, Muhle’s classification; mJOA; modified Japanese Orthopaedic Association score; PSA, posterior surgical approaches.

Figure 2

Machine learning (ML) process for this study. Five-fold cross-validation was repeated 50 times on the preprocessed training set to generate an optimal training model. Afterwards, test prediction was performed on the test set with the training model of each ML algorithm. RF, random forest; XGB, extreme gradient boosting.