Machine Learning Models for Analyzing Nerve Conduction Velocity

Document Type : Original Paper

Authors

1 Department of Physics, Faculty of Sciences, Arak University, Arak 38156-8-8349, Iran

2 Department of Radiotherapy and Medical Physics, Arak University of Medical Sciences and Khonsari Hospital, Arak, Iran

3 Industrial and Systems Engineering, Tarbiat Modares University, Tehran 4117-13114, Iran

10.22038/ijmp.2025.85526.2502

Abstract

Introduction: The objective of this study was to utilize Machine Learning (ML) techniques to assess the conduction of nerves located in the upper extremities, specifically the median, ulnar, and radial nerves. The study aimed to establish normal values for nerve conduction (NC) and evaluate the influence of variables such as gender, age, weight, and height on NC.
Material and Methods: Electrodiagnostic tests were employed to assess the conduction of both motor and sensory nerves. ML techniques were applied to analyze the data and predict NC values. The study considered historical background and thorough medical assessments to ensure the absence of any NC agents or underlying medical conditions.
Results: The investigation successfully established normal values for NC. The ML models demonstrated favorable performance in predicting NC values, considering the influence of variables such as gender, age, weight, and height.
Conclusion: The study successfully established normal values for nerve conduction in the upper extremities and demonstrated the effectiveness of ML models in predicting NC values. These findings highlight the potential of ML techniques in enhancing the assessment and understanding of nerve conduction, considering various influencing factors. However, this study has limitations, including its single-center design and a relatively small female cohort, which may affect the generalizability of the results.

Keywords

Main Subjects

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Iranian Journal of Medical Physics
Volume 22, Issue 5 - Serial Number 5
September and October 2025
Pages 350-360

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History

  • Receive Date: 22 January 2025
  • Revise Date: 09 December 2025
  • Accept Date: 04 August 2025

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