Pikatza-Huerga A, Las Hayas C, Zulaika U, Almeida A
Faculty of Engineering, Universidad de Deusto, Avenida Universidades, Bilbao, Spain.
Faculty of Health Sciences, Universidad de Deusto, Avenida Universidades, Bilbao, Spain.
Comput Struct Biotechnol J. 2025 Apr 2;28:118-127. doi: 10.1016/j.csbj.2025.03.048. eCollection 2025.
This 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 ED.
本研究旨在评估各种问卷在确定饮食失调(ED)风险以及预测个体康复水平方面的预测能力。该研究采用机器学习模型和多样的数据集,着重于了解不同问卷在洞察ED症状和康复结果方面的有效性。此外,该研究还试图识别对康复过程有显著影响的特征。这项调查旨在提供有价值的信息,以改进心理健康领域,特别是与ED相关的诊断和监测工具。