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

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          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.

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          Most cited references 20

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          Risk factors for the development of low back pain in adolescence.

          A previous history and earlier onset of low back pain are associated with chronic low back pain in adults, implying that prevention in adolescence may have a positive impact in adulthood. The study objectives were to determine the incidence of low back pain in a cohort of adolescents and to ascertain risk factors. A cohort of 502 high school students in Montreal, Canada, was evaluated during 1995-1996 at three separate times, 6 months apart. The outcome was low back pain occurrence at a frequency of at least once a week in the previous 6 months. Of the 377 adolescents who did not complain of low back pain at the initial evaluation, 65 developed low back pain over the year (cumulative incidence, 17 percent). Risk factors associated with development of low back pain were high growth (odds ratio = 3.09; 95 percent confidence interval (CI): 1.53, 6.01), smoking (odds ratio = 2.20; 95% CI: 1.38, 3.50), tight quadriceps femoris (odds ratio = 1.02; 95% CI: 1.00, 1.05), tight hamstrings (odds ratio = 1.04; 95% CI: 1.01, 1.06), and working during the school year (odds ratio = 1.33; 95% CI: 1.03, 1.71). Modifying such risk factors as smoking and poor leg flexibility may potentially serve to prevent the development of low back pain in adolescents.
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            Modic changes in endplates of lumbar vertebral bodies: prevalence and association with low back and sciatic pain among middle-aged male workers.

            Cross-sectional comparison of self-reported low back pain (LBP) symptoms and Modic findings on magnetic resonance imaging (MRI). To investigate associations of frequency and intensity of LBP and sciatic pain with Modic changes in a sample of middle-aged male workers with or without whole-body vibration exposure. Vertebral endplate changes are bone marrow lesions visible on MRI and are assumed to be associated with degenerative intervertebral disc disease. Associations of these so-called Modic changes with clinical symptoms are controversial. Furthermore, most of these studies have been performed in selected series of patients. A total of 228 middle-aged male workers (159 train engineers and 69 sedentary controls) from northern Finland underwent sagittal T1 and T2-weighted MRI. Both endplates of 1140 lumbar interspaces were graded for type and extent of Modic changes. Logistic regression was used to analyze associations of pain variables with Modic changes. Train engineers had on the average higher sciatic pain scores than the sedentary controls, but the prevalence of Modic changes was similar in both occupational groups. Altogether, 178 Modic changes in 128 subjects were recorded: 30% were type I, 66% type II, and 4% both types I and II. Eighty percent of changes occurred at L4-L5 or L5-S1. Modic changes at L5-S1 showed significant association with pain symptoms with increased frequency of LBP (odds ratio [OR] 2.28; 95% confidence interval [CI] 1.44-3.15) and sciatica episodes (OR 1.44; 95% CI 1.01-1.89), and with higher LBP visual analog scores during the past week (OR 1.36; 95% CI 1.06-1.70). Type I lesions and extensive lesions in particular were closely associated with pain. Modic changes at L5-S1 and Modic type I lesions are more likely to be associated with pain symptoms than other types of Modic changes or changes located at other lumbar levels.
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              Classification of EEG signals using neural network and logistic regression.

              Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.

                Author and article information

                J Pain Res
                J Pain Res
                Journal of Pain Research
                15 April 2019
                : 12
                : 1179-1187
                [1 ]Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences , Kerman, Iran
                [2 ]Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences , Kerman, Iran
                [3 ]Department of Neurology, Kerman University of Medical Sciences , Kerman, Iran
                Author notes
                Correspondence: Tania DeheshDepartment of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences , Haft Bagh Alavi, Kerman, 7617647633, IranTel +98 343 132 5069Email tania_dehesh@ 123456yahoo.com
                © 2019 Jafari et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                Page count
                Figures: 1, Tables: 4, References: 28, Pages: 9
                Original Research


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