{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:39:22Z","timestamp":1792172362084,"version":"4.4.0"},"reference-count":22,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>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\u2014Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Gradient Boosting\u2014were 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 \u00b1 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.<\/jats:p>","DOI":"10.3390\/life16071061","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:37:55Z","timestamp":1782391075000},"page":"1061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using Machine Learning-Based Classification of Postural Stability in Cervicogenic Headache Patients: Predictors and Clinical Implications"],"prefix":"10.3390","volume":"16","author":[{"given":"Mohamed Abdelaziz","family":"Emam","sequence":"first","affiliation":[{"name":"Basic Sciences Department, Faculty of Physical Therapy, Kafr El Sheikh University, Kafr El Sheikh 33511, Egypt"},{"name":"J\u00e1nos Szent\u00e1gothai Neurosciences Division, Semmelweis University, 1085 Budapest, Hungary"},{"name":"Neurocognitive Research Centre, Ny\u00edr\u0151 Gyula National Institute of Psychiatry and Addictology, 1135 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3495-7508","authenticated-orcid":false,"given":"Magda","family":"Ramadan","sequence":"additional","affiliation":[{"name":"Basic Sciences Department, Faculty of Physical Therapy, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andras Attila","family":"Horvath","sequence":"additional","affiliation":[{"name":"Neurocognitive Research Centre, Ny\u00edr\u0151 Gyula National Institute of Psychiatry and Addictology, 1135 Budapest, Hungary"},{"name":"Department of Anatomy, Histology and Embryology, Semmelweis University, 1094 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5009-4514","authenticated-orcid":false,"given":"Ahmed M.","family":"Kadry","sequence":"additional","affiliation":[{"name":"Faculty of Physical Therapy, Kafr El Sheikh University, Kafr El Sheikh 33511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gergo","family":"Bolla","sequence":"additional","affiliation":[{"name":"Neurocognitive Research Centre, Ny\u00edr\u0151 Gyula National Institute of Psychiatry and Addictology, 1135 Budapest, Hungary"},{"name":"School of PhD Studies, Semmelweis University, 57 Lehel \u00fat, 1135 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatma S.","family":"Amin","sequence":"additional","affiliation":[{"name":"Basic Sciences Department, Faculty of Physical Therapy, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed S. A.","family":"Youssef","sequence":"additional","affiliation":[{"name":"Department of Basic Science for Physical Therapy, Faculty of Physical Therapy, Beni-Suef University, Beni Suef 62521, Egypt"},{"name":"Rehabilitation & Diagnostic Sciences Department, Faculty of Medical and Health Sciences, Liwa University, Abu Dhabi P.O. Box 41009, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S194","DOI":"10.4103\/0028-3886.315992","article-title":"Cervicogenic headache: Current perspectives","volume":"69","author":"Verma","year":"2021","journal-title":"Neurol. 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