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Open Access

Peer-reviewed

Research Article

Applying binary mixed model to predict knee osteoarthritis pain

Helal El-Zaatari,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Thurston Arthitis Research Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America

https://orcid.org/0000-0003-0617-1559

⨯
Liubov Arbeeva,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Thurston Arthitis Research Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
⨯
Amanda E. Nelson

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: helalelzaatari@gmail.com

Affiliation Thurston Arthitis Research Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America

https://orcid.org/0000-0002-9344-7877

⨯

Applying binary mixed model to predict knee osteoarthritis pain

Helal El-Zaatari,
Liubov Arbeeva,
Amanda E. Nelson

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Published: July 15, 2025
https://doi.org/10.1371/journal.pone.0325678

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Knees
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Pain
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Osteoarthritis
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Stiffness
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Forecasting
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Machine learning
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Skeletal joints
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Support vector machines
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