{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:34:13Z","timestamp":1762432453956,"version":"3.41.2"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>This research work introduces a novel, nonintrusive method for the automatic identification of Smith\u2013Magenis syndrome, traditionally studied through genetic markers. The method utilizes cepstral peak prominence and various machine learning techniques, relying on a single metric computed by the research group. The performance of these techniques is evaluated across two case studies, each employing a unique data preprocessing approach. A proprietary data \u201cwindowing\u201d technique is also developed to derive a more representative dataset. To address class imbalance in the dataset, the synthetic minority oversampling technique (SMOTE) is applied for data augmentation. The application of these preprocessing techniques has yielded promising results from a limited initial dataset. The study concludes that the k-nearest neighbors and linear discriminant analysis perform best, and that cepstral peak prominence is a promising measure for identifying Smith\u2013Magenis syndrome.<\/jats:p>","DOI":"10.3389\/fncom.2024.1357607","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T14:35:27Z","timestamp":1711118127000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of Smith\u2013Magenis syndrome cases through an experimental evaluation of machine learning methods"],"prefix":"10.3389","volume":"18","author":[{"given":"Ra\u00fal","family":"Fern\u00e1ndez-Ruiz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"N\u00fa\u00f1ez-Vidal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene","family":"Hidalgo-delagu\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Garayz\u00e1bal-Heinze","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agust\u00edn","family":"\u00c1lvarez-Marquina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Mart\u00ednez-Olalla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Palacios-Alonso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ref1","first-page":"26","article-title":"Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach","author":"Abdar","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"104068","DOI":"10.1016\/j.ijmedinf.2019.104068","article-title":"Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer","volume":"136","author":"Alabi","year":"2020","journal-title":"Int. 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