دورية أكاديمية
Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
| العنوان: | Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years |
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| المؤلفون: | Jae-Geum Shim, Kyoung-Ho Ryu, Eun-Ah Cho, Jin Hee Ahn, Hong Kyoon Kim, Yoon-Ju Lee, Sung Hyun Lee |
| المصدر: | Medicina, Vol 57, Iss 11, p 1230 (2021) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Medicine (General) |
| مصطلحات موضوعية: | chronic lower back pain, machine learning, artificial neural network, logistic regression k-nearest neighbors, naïve Bayes, decision tree, Medicine (General), R5-920 |
| الوصف: | Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1648-9144 1010-660X |
| Relation: | https://www.mdpi.com/1648-9144/57/11/1230; https://doaj.org/toc/1010-660X; https://doaj.org/toc/1648-9144 |
| DOI: | 10.3390/medicina57111230 |
| URL الوصول: | https://doaj.org/article/18b154ed718a4afda00f4e53d0622d5e |
| رقم الأكسشن: | edsdoj.18b154ed718a4afda00f4e53d0622d5e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16489144 1010660X |
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| DOI: | 10.3390/medicina57111230 |