Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement - Research article

Aug 5, 2026

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Carpal tunnel syndrome (CTS) is the most prevalent upper-extremity entrapment neuropathy and involves multiple risk factors. This study evaluates whether hand anthropometric measurements together with grip strength and pinch strength can predict CTS using machine learning. Patients with CTS symptoms and asymptomatic controls were assessed with electrophysiological confirmation. Four common algorithms were trained with parameter tuning and cross-validation, and variable-importance results were reported. Random Forests and XGBoost showed the best performance, highlighting wrist circumference, hand width, grip and pinch measures, and middle finger length as key indicators.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Objectives

Methods

Results

Conclusion

Introduction

PLOS ONE OPEN ACCESS Citation: Yetiş M, Kocaman H, Canlı M, Yıldırım H, Yetiş A, Ceylan İ (2024) Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement. PLoS ONE 19(4): e0300044 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0300044 Editor: Yih-Kuen Jan, University of Illinois UrbanaChampaign, UNITED STATES Received: September 2, 2023 Accepted: February 20, 2024 Published: April 17, 2024 Copyright: © 2024 Yetiş et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: In terms of personal data protection and patient privacy law in our country, people who want to access the data of our study can access the data by obtaining permission from Kırşehir Ahi Evran University Ethics Committee. Below is the contact information of the ethics committee; E-mail address: [tipetikkurul@ahievran.edu.tr](tipetikkurul@ahievran.edu.tr). Funding: The authors received no specific funding for this work. The funders had no role in study RESEARCH ARTICLE Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement Mehmet Yetiş1, Hikmet Kocaman2¤ *, Mehmet Canlı3, Hasan Yıldırım4, Aysu Yetiş5,İsmail Ceylan3 1 Department of Orthopedics and Traumatology, Faculty of Medicine, Kırşehir Ahi Evran University, Kırşehir, Turkey, 2 Department of Physiotherapy and Rehabilitation / Prosthetics-Orthotics Physiotherapy, Karamanoglu Mehmetbey University, Karaman, Turkey, 3 School of Physical Therapy and Rehabilitation, Kırşehir Ahi Evran University, Kırşehir, Turkey, 4 Department of Mathematics, Faculty of Kamil ¨Ozdağ Science, Karamanoglu Mehmetbey University, Karaman, Turkey, 5 Department of Neurology, Faculty of Medicine, Kırşehir Ahi Evran University, Kırşehir, Turkey ¤ Current address: Department of Physiotherapy and Rehabilitation / Prosthetics-Orthotics Physiotherapy, Karamanoglu Mehmetbey University, Karaman, Turkey * [kcmnhikmet@gmail.com](kcmnhikmet@gmail.com) Abstract Objectives Carpal tunnel syndrome (CTS) stands as the most prevalent upper extremity entrapment neuropathy, with a multifaceted etiology encompassing various risk factors. This study aimed to investigate whether anthropometric measurements of the hand, grip strength, and pinch strength could serve as predictive indicators for CTS through machine learning techniques. Methods Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments. Anthropometric measurements of the hand were obtained using a digital caliper, grip strength was gauged via a digital handgrip dynamometer, and pinch strengths were assessed using apinchmeter. A comprehensive analysis was conducted employing four most common and effective machine learning algorithms, integrating thorough parameter tuning and cross-validation procedures. Additionally, the outcomes of variable importance were presented. Results Among the diverse algorithms, Random Forests (accuracy of 89 .474%, F1-score of 0 .905, and kappa value of 0 .789) and XGBoost (accuracy of 86 .842%, F1-score of 0 .878, and kappa value of 0 .736) emerged as the top-performing choices based on distinct classification metrics. In addition, using variable importance calculations specific to these models, the design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. most important variables were found to be wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length. Conclusion

FAQs

What data were used to predict carpal tunnel syndrome in this study?

The study used hand anthropometric measurements plus grip strength and pinch strength, combined for CTS prediction with machine learning algorithms.

How were participants classified as CTS or control?

Patients with CTS symptoms and asymptomatic healthy controls were enrolled, and CTS status was confirmed using electrophysiological assessments.

Which machine learning models performed best?

Random Forests and XGBoost achieved the highest performance based on multiple classification metrics.

Which variables were most important for CTS prediction?

Wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length were identified as the most important variables.

Aug 5, 2026

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