Predictive assessment of eating disorder risk and recovery: uncovering the effectiveness of questionnaires and influencing characteristics

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dc.contributor.authorPikatza Huerga, Amaia
dc.contributor.authorLas Hayas Rodríguez, Carlota
dc.contributor.authorZulaika Zurimendi, Unai
dc.contributor.authorAlmeida, Aitor
dc.date.accessioned2025-04-16T09:26:32Z
dc.date.available2025-04-16T09:26:32Z
dc.date.issued2025
dc.date.updated2025-04-16T09:26:32Z
dc.description.abstractThis study aims to assess the predictive capabilities of various questionnaires in determining the risk of Eating Disorders (ED) and predicting the level of recovery among individuals. Employing machine learning models and diverse datasets, the research focuses on understanding the effectiveness of different questionnaires in providing insights into ED symptoms and recovery outcomes. Additionally, the study seeks to identify the characteristics that significantly influence the recovery process. The investigation aims to contribute valuable information to enhance the diagnostic and monitoring tools used in the field of mental health, particularly concerning EDen
dc.identifier.citationPikatza-Huerga, Las Hayas, Zulaika, & Almeida. (2025). Predictive assessment of eating disorder risk and recovery: uncovering the effectiveness of questionnaires and influencing characteristics. Computational and Structural Biotechnology Journal, 28, 118-127. https://doi.org/10.1016/J.CSBJ.2025.03.048
dc.identifier.doi10.1016/J.CSBJ.2025.03.048
dc.identifier.eissn2001-0370
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2630
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2025 The Authors
dc.subject.otherEating disorders
dc.subject.otherFeature importance
dc.subject.otherMachine learning
dc.subject.otherQuestionnaires
dc.titlePredictive assessment of eating disorder risk and recovery: uncovering the effectiveness of questionnaires and influencing characteristicsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage127
oaire.citation.startPage118
oaire.citation.titleComputational and Structural Biotechnology Journal
oaire.citation.volume28
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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