Jove
Visualize
Contact Us

Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Multimarker Model for Prostate Cancer Risk Assessment: Improving Diagnostic Accuracy Beyond PSA.

The Prostate·2025
Same author

A Glycolysis and gluconeogenesis-related model for breast cancer prognosis.

Cancer biomarkers : section A of Disease markers·2025
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 12, 2026

Studying Diabetes Through the Eyes of a Fish: Microdissection, Visualization, and Analysis of the Adult tgfli:EGFP Zebrafish Retinal Vasculature
10:07

Studying Diabetes Through the Eyes of a Fish: Microdissection, Visualization, and Analysis of the Adult tgfli:EGFP Zebrafish Retinal Vasculature

Published on: December 26, 2017

13.1K

Development and validation of predictive models for diabetic retinopathy using machine learning.

Penglu Yang1, Bin Yang2

  • 1The First Clinical School & Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Plos One
|February 24, 2025
PubMed
Summary

Machine learning models, specifically random forest and XGBoost, show high accuracy in predicting diabetic retinopathy (DR). These models utilize key markers like HbA1c and serum creatinine for early detection and improved diabetes management.

More Related Videos

An Ex Vivo Tissue Culture Model for Fibrovascular Complications in Proliferative Diabetic Retinopathy
08:10

An Ex Vivo Tissue Culture Model for Fibrovascular Complications in Proliferative Diabetic Retinopathy

Published on: January 25, 2019

7.7K
Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression
04:36

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

1.0K

Related Experiment Videos

Last Updated: May 12, 2026

Studying Diabetes Through the Eyes of a Fish: Microdissection, Visualization, and Analysis of the Adult tgfli:EGFP Zebrafish Retinal Vasculature
10:07

Studying Diabetes Through the Eyes of a Fish: Microdissection, Visualization, and Analysis of the Adult tgfli:EGFP Zebrafish Retinal Vasculature

Published on: December 26, 2017

13.1K
An Ex Vivo Tissue Culture Model for Fibrovascular Complications in Proliferative Diabetic Retinopathy
08:10

An Ex Vivo Tissue Culture Model for Fibrovascular Complications in Proliferative Diabetic Retinopathy

Published on: January 25, 2019

7.7K
Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression
04:36

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

1.0K

Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Ophthalmology

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients.
  • Early detection and intervention are crucial for preventing severe visual impairment.
  • Predictive modeling can aid in identifying at-risk individuals.

Purpose of the Study:

  • To develop and compare machine learning models for predicting diabetic retinopathy (DR).
  • To evaluate the efficacy of logistic regression, random forest, XGBoost, and neural networks using clinical and biochemical data.
  • To identify key predictors for early DR detection.

Main Methods:

  • Utilized a dataset of 3,000 diabetic patients (1,500 with DR) from the National Population Health Science Data Center.
  • Developed and compared four machine learning models: logistic regression, random forest, XGBoost, and neural networks.
  • Assessed model performance using accuracy, precision, recall, F1-score, and area under the curve (AUC).

Main Results:

  • Random forest (95.67% accuracy, 0.991 AUC) and XGBoost (94.67% accuracy, 0.989 AUC) exhibited superior predictive performance.
  • Logistic regression achieved 76.50% accuracy (AUC: 0.828), and neural networks achieved 82.67% accuracy (AUC: 0.927).
  • Significant predictors included 24-hour urinary microalbumin, HbA1c, and serum creatinine.

Conclusions:

  • Random forest and XGBoost are highly effective for early diabetic retinopathy detection.
  • Renal and glycemic markers are critical for assessing DR risk.
  • Integration of these machine learning models can enhance clinical decision-making and patient outcomes in diabetes care.