An investigation of machine learning algorithms for prediction of lumbar disc herniation

In this study, LDH was predicted using machine learning techniques using measures of the lumbar paraspinal muscles, lumbar vessels cross-s ectional area (CSA), and lumbar sagittal curve. Three hundred and forty-four individuals’ MR scans were prospectively enrolled (264 with LDH and 80 healthy). Predictive factors were the lumbar sagittal curve and the cross-sectional areas of the lumbar paraspinal muscles and vessels from sagittal a nd axial MR images. The measurements have been analyzed via ten different and most common machine learning algorithms by considering a comprehensive parameter tuning and cross-validation process. The variable importance results have been also presented. XGBoost algorithm among all algorithms has pro vided the best results in terms of different classification metrics including f-score (\(0.830\)), AUC (\(0.939\)), accuracy (\(0.922\)), and kappa (\(0.779\)). The findings of this study demonstrated that cross-sectional areas of the quadratus lumborum and abdominal aorta can be utilized as a reliable indicator of LDH. Consequently, the developed model and the variables found to be important may guide to healthcare professionals to make more accurate and effective decisions in terms of prediction the LDH.Graphical abstract
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research