Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data

Authors

Ali Al-Ramini
Mahdi Hassan, University of Nebraska at Omaha
Farah Fallahtafti, University of Nebraska at OmahaFollow
Mohammad Ali TakallouFollow
Hafizur Rahman, University of Nebraska at OmahaFollow
Basheer Qolomany
Iraklis Pipinos, University of Nebraska at OmahaFollow
Fadi Alsaleem
Sara A. Myers, University of Nebraska at OmahaFollow

Author ORCID Identifier

Myers - https://orcid.org/0000-0002-2934-2624

Rahman - https://orcid.org/0000-0003-1739-3544

Fallahtafti - https://orcid.org/0000-0003-1739-3544

Pipinos - https://orcid.org/0000-0001-6873-6346

Document Type

Article

Publication Date

8-30-2022

Abstract

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification.

Comments

This is an Open Access article licensed under CC BY

DOI: https://doi.org/10.3390/s22197432

Journal Title

Sensors

Recommended Citation

Al-Ramini, Ali; Hassan, Mahdi; Fallahtafti, Farah; Takallou, Mohammad Ali; Rahman, Hafizur; Qolomany, Basheer; Pipinos, Iraklis; Alsaleem, Fadi; and Myers, Sara A., "Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data" (2022). Journal Articles. 346.
https://digitalcommons.unomaha.edu/biomechanicsarticles/346

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.