ArticlePloS one2014
Application of random forests methods to diabetic retinopathy classification analyses.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- Machine learning models for risk prediction of diabetic retinopathy in Fujian eye study.Frontiers in endocrinology · 2026Article
- [Adaptive lesion-aware fusion network for joint grading of multiple fundus diseases].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Application of Machine Learning to Discriminate Photoreceptor Cell Species in Xenotransplanted Chimeric Retinas.Clinical and translational science · 2025Article
- Interpreting Deep Neural Networks in Diabetic Retinopathy Grading: A Comparison with Human Decision Criteria.Life (Basel, Switzerland) · 2025Article
- Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.Communications medicine · 2025Article
- An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.Scientific reports · 2024Article
- Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method.BMC medical informatics and decision making · 2024Article
- Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.GeroScience · 2024Article
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- Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.Lipids in health and disease · 2024Article
- A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.Quantitative imaging in medicine and surgery · 2024Article
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- Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?Life (Basel, Switzerland) · 2022Article
- Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Characterization of spleen and lymph node cell typesFrontiers in molecular neuroscience · 2022Article
- Drug-Drug Interactions Prediction Using Fingerprint Only.Computational and mathematical methods in medicine · 2022Article
- Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.Frontiers in bioengineering and biotechnology · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundDiabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. METHODOLOGY: Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. PRINCIPAL
findingsBoth types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. CONCLUSIONS AND SIGNIFICANCE: We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.
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