Machine learning-based approach for disease severity classification of carpal tunnel syndrome

Abstract Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was...

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Autores principales:	Dougho Park, Byung Hee Kim, Sang-Eok Lee, Dong Young Kim, Mansu Kim, Heum Dai Kwon, Mun-Chul Kim, Ae Ryoung Kim, Hyoung Seop Kim, Jang Woo Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3511bd74213240468a6fe814db336306
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spelling	oai:doaj.org-article:3511bd74213240468a6fe814db3363062021-12-02T19:04:36ZMachine learning-based approach for disease severity classification of carpal tunnel syndrome10.1038/s41598-021-97043-72045-2322https://doaj.org/article/3511bd74213240468a6fe814db3363062021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97043-7https://doaj.org/toc/2045-2322Abstract Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.Dougho ParkByung Hee KimSang-Eok LeeDong Young KimMansu KimHeum Dai KwonMun-Chul KimAe Ryoung KimHyoung Seop KimJang Woo LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dougho Park Byung Hee Kim Sang-Eok Lee Dong Young Kim Mansu Kim Heum Dai Kwon Mun-Chul Kim Ae Ryoung Kim Hyoung Seop Kim Jang Woo Lee Machine learning-based approach for disease severity classification of carpal tunnel syndrome
description	Abstract Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.
format	article
author	Dougho Park Byung Hee Kim Sang-Eok Lee Dong Young Kim Mansu Kim Heum Dai Kwon Mun-Chul Kim Ae Ryoung Kim Hyoung Seop Kim Jang Woo Lee
author_facet	Dougho Park Byung Hee Kim Sang-Eok Lee Dong Young Kim Mansu Kim Heum Dai Kwon Mun-Chul Kim Ae Ryoung Kim Hyoung Seop Kim Jang Woo Lee
author_sort	Dougho Park
title	Machine learning-based approach for disease severity classification of carpal tunnel syndrome
title_short	Machine learning-based approach for disease severity classification of carpal tunnel syndrome
title_full	Machine learning-based approach for disease severity classification of carpal tunnel syndrome
title_fullStr	Machine learning-based approach for disease severity classification of carpal tunnel syndrome
title_full_unstemmed	Machine learning-based approach for disease severity classification of carpal tunnel syndrome
title_sort	machine learning-based approach for disease severity classification of carpal tunnel syndrome
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3511bd74213240468a6fe814db336306
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Machine learning-based approach for disease severity classification of carpal tunnel syndrome

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