Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years

<i>Background and Objectives</i>: 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 t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jae-Geum Shim, Kyoung-Ho Ryu, Eun-Ah Cho, Jin Hee Ahn, Hong Kyoon Kim, Yoon-Ju Lee, Sung Hyun Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	chronic lower back pain machine learning artificial neural network logistic regression k-nearest neighbors naïve Bayes decision tree Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/18b154ed718a4afda00f4e53d0622d5e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:18b154ed718a4afda00f4e53d0622d5e
record_format	dspace
spelling	oai:doaj.org-article:18b154ed718a4afda00f4e53d0622d5e2021-11-25T18:18:47ZMachine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years10.3390/medicina571112301648-91441010-660Xhttps://doaj.org/article/18b154ed718a4afda00f4e53d0622d5e2021-11-01T00:00:00Zhttps://www.mdpi.com/1648-9144/57/11/1230https://doaj.org/toc/1010-660Xhttps://doaj.org/toc/1648-9144<i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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). <i>Results</i>: 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. <i>Conclusions</i>: 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.Jae-Geum ShimKyoung-Ho RyuEun-Ah ChoJin Hee AhnHong Kyoon KimYoon-Ju LeeSung Hyun LeeMDPI AGarticlechronic lower back painmachine learningartificial neural networklogistic regression k-nearest neighborsnaïve Bayesdecision treeMedicine (General)R5-920ENMedicina, Vol 57, Iss 1230, p 1230 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	chronic lower back pain machine learning artificial neural network logistic regression k-nearest neighbors naïve Bayes decision tree Medicine (General) R5-920
spellingShingle	chronic lower back pain machine learning artificial neural network logistic regression k-nearest neighbors naïve Bayes decision tree Medicine (General) R5-920 Jae-Geum Shim Kyoung-Ho Ryu Eun-Ah Cho Jin Hee Ahn Hong Kyoon Kim Yoon-Ju Lee Sung Hyun Lee Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
description	<i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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). <i>Results</i>: 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. <i>Conclusions</i>: 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.
format	article
author	Jae-Geum Shim Kyoung-Ho Ryu Eun-Ah Cho Jin Hee Ahn Hong Kyoon Kim Yoon-Ju Lee Sung Hyun Lee
author_facet	Jae-Geum Shim Kyoung-Ho Ryu Eun-Ah Cho Jin Hee Ahn Hong Kyoon Kim Yoon-Ju Lee Sung Hyun Lee
author_sort	Jae-Geum Shim
title	Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
title_short	Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
title_full	Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
title_fullStr	Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
title_full_unstemmed	Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years
title_sort	machine learning approaches to predict chronic lower back pain in people aged over 50 years
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/18b154ed718a4afda00f4e53d0622d5e
work_keys_str_mv	AT jaegeumshim machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years AT kyounghoryu machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years AT eunahcho machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years AT jinheeahn machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years AT hongkyoonkim machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years AT yoonjulee machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years AT sunghyunlee machinelearningapproachestopredictchroniclowerbackpaininpeopleagedover50years
_version_	1718411304988835840

Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years

Ejemplares similares