{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:12:39Z","timestamp":1788966759611,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Post-COVID-19 condition (PCC) is a commonly reported disorder that has gained attention from the World Health Organization (WHO). Several studies have examined factors associated with PCC; however, relatively few have combined statistical and machine-learning approaches. Therefore, this study used statistical analysis to identify factors associated with PCC and machine-learning methods to evaluate the relative importance of these factors and their contributions to model predictions of PCC. Materials and Methods: This study employed a cross-sectional observational design in which 963 eligible individuals who had tested positive for COVID-19 were enrolled. Participants were asked about the presence of persistent symptoms lasting for at least 2 months and occurring 3 months after COVID-19 infection, as well as the specific symptoms experienced. The WHO Global COVID-19 Clinical Platform Case Report Form for PCC was used to classify persistent symptoms. Demographic information and medical factors were examined using Poisson regression and machine-learning techniques. Results: A total of 209 (22%) out of 963 reported having PCC with fatigue (45%), followed by bone\/joint\/muscle pain (34%), one neurological symptom (24%), one pulmonary\/respiratory symptom (23%), and one mental health symptom (16%) were the most common persistent symptoms. Modified Poisson regression showed that having exactly two chronic conditions, and experiencing two or more previous COVID-19 infections were significantly associated with the prevalence of PCC. The SHAP beeswarm plot indicated that sex, age, time since last COVID-19 infection, number of chronic conditions, and BMI had the greatest influence on the support vector machine (SVM) predictions. Within the fitted model, female sex, age, a longer time since last COVID-19 infection, the presence of chronic conditions, and higher BMI generally shifted predictions toward the PCC category. Conclusions: Approximately 22% of participants reported persistent symptoms, with fatigue being the most frequently reported, followed by musculoskeletal pain and symptoms affecting other body systems. In the modified Poisson regression analysis, having exactly two chronic conditions and multiple previous COVID-19 infections were significantly associated with higher prevalence of PCC. However, the machine-learning models demonstrated limited discriminative performance.<\/jats:p>","DOI":"10.3390\/medicina62091739","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:02:48Z","timestamp":1788962568000},"page":"1739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Reported Post-COVID-19 Condition and Associated Factors Using Machine Learning Techniques: A Cross-Sectional Study"],"prefix":"10.3390","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4815-5652","authenticated-orcid":false,"given":"Alaa A.","family":"Alghwiri","sequence":"first","affiliation":[{"name":"Industrial Engineering, German Jordan University, Madaba 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7675-5835","authenticated-orcid":false,"given":"Ziad","family":"Hawamdeh","sequence":"additional","affiliation":[{"name":"School of Medicine, The University of Jordan, Amman 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-6334","authenticated-orcid":false,"given":"Abrar F. AlAbed","family":"Alhaq","sequence":"additional","affiliation":[{"name":"Information Systems, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9621-1960","authenticated-orcid":false,"given":"Dania F.","family":"Naser","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Nobles Rehabilitation Center, Amman 11194, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2122-9280","authenticated-orcid":false,"given":"Alia A.","family":"Alghwiri","sequence":"additional","affiliation":[{"name":"School of Kinesiology, Auburn University, Auburn, AL 36849, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","unstructured":"Ghebreyesus, T. (2026, May 01). 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