Predictive Models Utilizing Machine Learning for Visual Impairment in Chinese Adults Aged 45 and Older with Cardiovascular Metabolic Conditions: Insights from a Population-Based Analysis Using CHARLS

By
Yuhao Liu
Riyan Zhang
Duoduo Xie
Min Liu
Guanshun Yu
Zhong Lin
Jia Qu
Ronghan Wu
December 30, 2025
0 min

Bmc Ophthalmology

1

Visual impairment affects over 2.2 billion people globally and significantly impacts quality of life and mental health.
2

Cardiometabolic diseases are linked to an increased risk of visual impairment due to damage to the retina and optic nerve.
3

Timely assessment of visual impairment in cardiometabolic disease patients is challenging due to reliance on standard ophthalmic examinations.
4

This study developed machine learning models using CHARLS data to predict visual impairment in Chinese adults aged 45 and older with cardiometabolic conditions.
5

Key predictors of visual impairment were identified, and the models aim to facilitate early intervention and improve patient outcomes.

Original Source(s)

Bmc Ophthalmology

Predictive Models Utilizing Machine Learning for Visual Impairment in Chinese Adults Aged 45 and Older with Cardiovascular Metabolic Conditions: Insights from a Population-Based Analysis Using CHARLS

by Yuhao Liu, Riyan Zhang, Duoduo Xie, Min Liu, Guanshun Yu, Zhong Lin, Jia Qu, Ronghan Wu
December 30, 2025

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