Academic Journal
Using Machine Learning-Based Classification of Postural Stability in Cervicogenic Headache Patients: Predictors and Clinical Implications.
| Title: | Using Machine Learning-Based Classification of Postural Stability in Cervicogenic Headache Patients: Predictors and Clinical Implications. |
|---|---|
| Authors: | Emam, Mohamed Abdelaziz, Ramadan, Magda, Horvath, Andras Attila, Kadry, Ahmed M., Bolla, Gergo, Amin, Fatma S., Youssef, Ahmed S. A. |
| Source: | Life (2075-1729); Jul2026, Vol. 16 Issue 7, p1061, 14p |
| Subject Terms: | Postural balance, Sensorimotor integration, Pain measurement, Machine learning, Prediction models, Neck pain, Physical therapy, Balance disorders |
| Abstract: | Background: Cervicogenic headache (CEH) is a secondary headache disorder originating from dysfunction in the cervical spine. In addition to pain, individuals with CEH frequently experience disturbances in postural control and sensorimotor integration, which may compromise functional capacity and quality of life. Conventional clinical assessments typically focus on pain intensity and cervical range of motion; however, these measures often fail to capture the multifactorial mechanisms underlying balance impairments in this population. Machine learning (ML) methods offer the ability to integrate multidimensional clinical data and may provide a more comprehensive approach for identifying patterns of postural stability and the factors influencing balance regulation in CEH. Methods: A secondary analysis was conducted using baseline data pooled from three registered randomized controlled trials, comprising 68 independent participants diagnosed by a neurologist according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Postural Stability Class served as the primary outcome and was derived from quantitative stability scores categorized as High, Moderate, or Low. Predictor variables included demographic characteristics (age, gender), clinical measures (pain intensity, headache frequency, symptom duration, cervical range of motion), and sensorimotor parameters (center-of-pressure sway and gaze accuracy). Five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Gradient Boosting—were trained and evaluated using 10-fold cross-validation with procedures implemented to reduce overfitting. Results: The Gradient Boosting classifier demonstrated the best performance, achieving an accuracy of 0.857 and an F1 score of 0.857, with a cross-validated accuracy of 0.802 ± 0.063. Random Forest and XGBoost achieved accuracies of 0.786. Feature importance analysis identified center-of-pressure sway and pain intensity as the most influential predictors of stability classification, followed by cervical flexion range of motion and gaze accuracy. Demographic variables showed minimal contribution to model performance. Conclusions: Machine learning models were able to distinguish different levels of postural stability in individuals with CEH. The findings highlight the central role of pain and sensorimotor control in balance regulation and suggest that predictive analytics may support precision physiotherapy by enabling rehabilitation strategies tailored to individual sensorimotor profiles. [ABSTRACT FROM AUTHOR] |
| Copyright of Life (2075-1729) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20751729&ISBN=&volume=16&issue=7&date=20260701&spage=1061&pages=1061-1074&title=Life (2075-1729)&atitle=Using%20Machine%20Learning-Based%20Classification%20of%20Postural%20Stability%20in%20Cervicogenic%20Headache%20Patients%3A%20Predictors%20and%20Clinical%20Implications.&aulast=Emam%2C%20Mohamed%20Abdelaziz&id=DOI:10.3390/life16071061 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 195799115 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42175292969 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Using Machine Learning-Based Classification of Postural Stability in Cervicogenic Headache Patients: Predictors and Clinical Implications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Emam%2C+Mohamed+Abdelaziz%22">Emam, Mohamed Abdelaziz</searchLink><br /><searchLink fieldCode="AR" term="%22Ramadan%2C+Magda%22">Ramadan, Magda</searchLink><br /><searchLink fieldCode="AR" term="%22Horvath%2C+Andras+Attila%22">Horvath, Andras Attila</searchLink><br /><searchLink fieldCode="AR" term="%22Kadry%2C+Ahmed+M%2E%22">Kadry, Ahmed M.</searchLink><br /><searchLink fieldCode="AR" term="%22Bolla%2C+Gergo%22">Bolla, Gergo</searchLink><br /><searchLink fieldCode="AR" term="%22Amin%2C+Fatma+S%2E%22">Amin, Fatma S.</searchLink><br /><searchLink fieldCode="AR" term="%22Youssef%2C+Ahmed+S%2E+A%2E%22">Youssef, Ahmed S. A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Life (2075-1729); Jul2026, Vol. 16 Issue 7, p1061, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Postural+balance%22">Postural balance</searchLink><br /><searchLink fieldCode="DE" term="%22Sensorimotor+integration%22">Sensorimotor integration</searchLink><br /><searchLink fieldCode="DE" term="%22Pain+measurement%22">Pain measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Neck+pain%22">Neck pain</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+therapy%22">Physical therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Balance+disorders%22">Balance disorders</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Cervicogenic headache (CEH) is a secondary headache disorder originating from dysfunction in the cervical spine. In addition to pain, individuals with CEH frequently experience disturbances in postural control and sensorimotor integration, which may compromise functional capacity and quality of life. Conventional clinical assessments typically focus on pain intensity and cervical range of motion; however, these measures often fail to capture the multifactorial mechanisms underlying balance impairments in this population. Machine learning (ML) methods offer the ability to integrate multidimensional clinical data and may provide a more comprehensive approach for identifying patterns of postural stability and the factors influencing balance regulation in CEH. Methods: A secondary analysis was conducted using baseline data pooled from three registered randomized controlled trials, comprising 68 independent participants diagnosed by a neurologist according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Postural Stability Class served as the primary outcome and was derived from quantitative stability scores categorized as High, Moderate, or Low. Predictor variables included demographic characteristics (age, gender), clinical measures (pain intensity, headache frequency, symptom duration, cervical range of motion), and sensorimotor parameters (center-of-pressure sway and gaze accuracy). Five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Gradient Boosting—were trained and evaluated using 10-fold cross-validation with procedures implemented to reduce overfitting. Results: The Gradient Boosting classifier demonstrated the best performance, achieving an accuracy of 0.857 and an F1 score of 0.857, with a cross-validated accuracy of 0.802 ± 0.063. Random Forest and XGBoost achieved accuracies of 0.786. Feature importance analysis identified center-of-pressure sway and pain intensity as the most influential predictors of stability classification, followed by cervical flexion range of motion and gaze accuracy. Demographic variables showed minimal contribution to model performance. Conclusions: Machine learning models were able to distinguish different levels of postural stability in individuals with CEH. The findings highlight the central role of pain and sensorimotor control in balance regulation and suggest that predictive analytics may support precision physiotherapy by enabling rehabilitation strategies tailored to individual sensorimotor profiles. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Life (2075-1729) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=195799115 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/life16071061 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1061 Subjects: – SubjectFull: Postural balance Type: general – SubjectFull: Sensorimotor integration Type: general – SubjectFull: Pain measurement Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Neck pain Type: general – SubjectFull: Physical therapy Type: general – SubjectFull: Balance disorders Type: general Titles: – TitleFull: Using Machine Learning-Based Classification of Postural Stability in Cervicogenic Headache Patients: Predictors and Clinical Implications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Emam, Mohamed Abdelaziz – PersonEntity: Name: NameFull: Ramadan, Magda – PersonEntity: Name: NameFull: Horvath, Andras Attila – PersonEntity: Name: NameFull: Kadry, Ahmed M. – PersonEntity: Name: NameFull: Bolla, Gergo – PersonEntity: Name: NameFull: Amin, Fatma S. – PersonEntity: Name: NameFull: Youssef, Ahmed S. A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20751729 Numbering: – Type: volume Value: 16 – Type: issue Value: 7 Titles: – TitleFull: Life (2075-1729) Type: main |
| ResultId | 1 |