{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:17:44Z","timestamp":1765808264483,"version":"3.48.0"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Vasovagal syncope (VVS) affects 17% of children, significantly impairing quality of life. Machine learning (ML) models achieve high predictive accuracy of VVS in adults using blood pressure (BP) monitoring, but pediatric implementation remains challenging. The aim of the study was to evaluate whether ML models incorporating anthropometric data and heart rate variability (HRV) can predict VVS without BP monitoring in children with prior syncope or suspected VVS. We analyzed 87 participants (7\u201318 years) with VVS history. HRV indices (time-domain, frequency-domain, and nonlinear) were extracted from 5 min supine and standing ECG recordings using NeuroKit2. Multiple algorithms were tested with 10-fold cross-validation; SHAP analysis identified feature importance. AdaBoost achieved the performance of 71.0% accuracy, 76.3% sensitivity, and 63.3% specificity\u201478% of adult BP-dependent algorithm sensitivity. Weight, multifractal detrended fluctuation analysis during standing, and normalized low-frequency power were most influential. Alterations in symbolic dynamics and multiscale entropy indicated compromised autonomic complexity. ML models with anthropometric and HRV data show potential as an adjunctive screening tool to identify children at higher risk for syncope recurrence, requiring clinical confirmation.<\/jats:p>","DOI":"10.3390\/make7040166","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T13:32:37Z","timestamp":1765805557000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Prediction of Recurrent Vasovagal Syncope in Children Using Heart Rate Variability and Anthropometric Data\u2014A Pilot Study"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8711-0891","authenticated-orcid":false,"given":"Piotr","family":"Wieniawski","sequence":"first","affiliation":[{"name":"Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, 02-091 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2009-2200","authenticated-orcid":false,"given":"Jakub S.","family":"G\u0105sior","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, 02-091 Warsaw, Poland"}]},{"given":"Maciej","family":"Roso\u0142","sequence":"additional","affiliation":[{"name":"Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8549-7973","authenticated-orcid":false,"given":"Marcel","family":"M\u0142y\u0144czak","sequence":"additional","affiliation":[{"name":"Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, 02-525 Warsaw, Poland"}]},{"given":"Ewa","family":"Smereczy\u0144ska-Wierzbicka","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, 02-091 Warsaw, Poland"}]},{"given":"Anna","family":"Pi\u00f3recka-Maku\u0142a","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, 02-091 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3810-2822","authenticated-orcid":false,"given":"Rados\u0142aw","family":"Pietrzak","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, 02-091 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, R., Kang, Y., Zhang, M., Wang, H., Shi, L., and Li, X. 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