XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults

XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults

Abstract The global increase in dementia cases highlights the importance of early detection and intervention, particularly for individuals at risk of mild cognitive impairment (MCI), a precursor to dementia. The aim of this study is to develop and validate machine learning (ML) models based on non-i...

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Main Authors: Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Aging
Dementia
Early diagnosis
Machine learning
Mil cognitive impairment
XGBoost
Online Access:https://doi.org/10.1038/s41598-025-14832-0
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https://doi.org/10.1038/s41598-025-14832-0

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