Developing predictive models for early detection of intervertebral disc degeneration risk

Abstract

Intervertebral disc degeneration (IVDD) has been associated with causing chronic back pain. Lower back pain is a common cause of doctor’s visits and disability and affects 11.9% of the worldwide population. To the best of our knowledge, this work is the first to develop a tool for predicting the risk of IVDD using machine learning algorithms (MLAs) to aid in the earlier detection of the disease. MLAs utilized in this study include logistic regression, decision tree, artificial neural network, boosting, bagging, and random forest. The Synthetic Minority Oversampling Technique (SMOTE) method was performed on the unbalanced data set to up sample the minority class (patients with disc degeneration). The cluster-then-predict technique was also utilized to determine clusters in the data to be used as another risk factor or input variable into the classification algorithms. Results showed that the bagging algorithm developed the best-performing model. Results also demonstrated that the performance of the models improved after using the cluster-then-predict technique, which supports previous literature. Moreover, an improvement in the prediction of the minority class was also observed when the SMOTE method was applied. The finding from this study can be utilized to develop a predictive tool for the early detection of disc degeneration. This predictive tool can be used by doctors and physicians in the office with patients to closely monitor and assess disc degeneration risk.