Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability

Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability

Abstract Objective This study aims to develop an enhanced Transformer model for predicting mild cognitive impairment (MCI) using data from the China Health and Retirement Longitudinal Study (CHARLS), focusing on handling mixed data types and improving predictive accuracy. Methods The Transformer int...

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Bibliographic Details
Main Authors: Kai Ma, Junzhi Zhang, Xinhang Huang, Mengyang Wang
Format: Article
Language:English
Published: BMC 2025-02-01
Series:BMC Public Health
Subjects:
Mild cognitive impairment (MCI)
Alzheimer’s disease (AD)
Transformer model
Machine learning
Neurodegenerative disease prediction
Online Access:https://doi.org/10.1186/s12889-025-21762-z
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https://doi.org/10.1186/s12889-025-21762-z

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