Pikatza Huerga, AmaiaLas Hayas Rodríguez, CarlotaZulaika Zurimendi, UnaiAlmeida, Aitor2025-04-162025-04-162025Pikatza-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.2025.03.04810.1016/J.CSBJ.2025.03.048http://hdl.handle.net/20.500.14454/2630This 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 EDeng© 2025 The AuthorsEating disordersFeature importanceMachine learningQuestionnairesjournal article2025-04-162001-0370