Predicting Mild Cognitive Impairment in Type 2 Diabetes: A Machine Learning Approach.

ABSTRACT:

Background

Diabetes significantly increases the risk of cognitive impairment, particularly mild cognitive impairment (MCI). Early identification of individuals at risk for MCI is crucial for timely intervention. This study was aimed at developing and validating a machine learning-based model to predict MCI in patients with Type 2 diabetes (T2DM).

Methods

Participants with T2DM and completed cognitive assessments were included. Feature selection was done using statistical methods and genetic programming to reduce collinearity. Six classification models were trained and evaluated using cross-validation and hyperparameter tuning. External validation was performed with cohorts from the Jiangsu DiabEtes COgnitive Dysfunction Early Diagnosis and Intervention (DECODE) study and the

SUBMITTER: Li F 

PROVIDER: S-EPMC12554921 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature