Using Traditional Machine Learning Techniques for Predicting the Best Clinical Outcomes On Improvement In Vas, Mod And Ncos Based Upon Clinical And Imaging Features
To use machine learning techniques for predicting the clinical outcomes on improvement in VAS, MOD, and NCOS based on the managements like Spinal decompression without fusion (open discectomy/Laminectomy), Spinal compression with fusion, and Conservative Management.
METHODS OR BACKGROUND:
Patients with symptomatic lumbar spine disease with back pain with or without radiculopathy and neurological deficit were enrolled. The primary outcome measures were Visual analogue scale (VAS), Modified Oswestry Disability Index (MOD), and Neurogenic Claudication Outcome Score (NCOS) collected at pre-operatively and at 3 months post-operatively. The further analysis studied the following factors to determine if any are predictive of outcomes: sex, BMI, occupation, involvement in sports, herniation type, depression, work status, herniation level, duration of symptoms, and history of past spine surgery. The features were selected and machine learning models were trained to predict the improvement in the primary outcome measures. The results were evaluated on the basis of the ROC-AUC score for different classes.
RESULTS OR FINDINGS:
There were a total of 200 entries of patients with Lumbar Spine Disease between age 18 and above. Among the various Machine Learning Models, Random Forest Classifier gave the best ROC-AUC score in all three classes. The AUC score for VAS, MOD, and NCOS was 0.877, 0.8215, and 0.830 respectively and the macro-average AUC score was found to be 0.84 (see Fig.1).
Fig 1: ROC-AUC scores of different Machine Learning Algorithms on Test Dataset.
Machine Learning model could be used as a predictive tool for deciding the type of management that a patient should undergo to achieve the best results. Based on the predicted improvement in different indices for the particular management type, the predictions could help Surgeons for deciding the type of management that would be most beneficial for the patient.