Classifying patients with lumbar disc herniation and exploring the most effective risk factors for this disease

Abstract

Objectives: To classify patients suffering from low back pain (LBP) into two different groups - patients with lumbar disc herniation (LDH) and patients without this disease based on simple questions and without magnetic resonance imaging (MRI) procedure - and to diagnose the most effective risk factors of LDH. Methods: Four hundred patients aged over 18 years suffering from LBP for over 6 months were randomized into two groups in this cross-sectional study. The data were gathered at Besat clinic, in Kerman, southeast of Iran. Twelve dichotomous questions from the main LDH risk factors were asked. Three statistical classification methods - K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR) - were performed. LR was used in order to diagnose the most important risk factors of LDH. Results: SVM method was more efficient among the small sample sizes, while KNN method showed the best classification relative to other methods when the sample size increased. LR model had the least efficiency of all. The drug use increased the chance of LDH more than 7 times (OR=7.249), and the chance of having LDH among people who had associated illness was 4.847 times more compared with people who did not have. Using hookah increased the chance of having LDH more than twice (OR=2.401), and the chance of smokers for LDH was near four times higher than nonsmokers (OR=3.877). Conclusion: The statistical classification methods had acceptable precisions for diagnosis of LDH patients. It is suggested that neurologists become more familiar with these methods and use them before MRI prescription to decrease the unnecessary burden on health services. Addiction to drugs, cigarettes, and hookah is the main factor in the creation of a lumbar disc herniation.