ArticleHealthcare (Basel, Switzerland)2022
Classification Algorithms Used in Predicting Glaucoma Progression.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Performance of OCT-Based Artificial Intelligence Models for Detecting and Predicting Glaucoma Progression: A Systematic Review.Journal of clinical medicine · 2026Review
- Sparse-Observation Multi-Horizon Glaucoma Progression Forecasting with Biologically Constrained Temporal Consistency: A Glaucoma Case Study.Research square · 2026Article
- Myopia as a Global Public Health Challenge a Narrative Review.Life (Basel, Switzerland) · 2026Review
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma.Life (Basel, Switzerland) · 2025Article
- Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.Current eye research · 2025Article
- The use of artificial neural networks in studying the progression of glaucoma.Scientific reports · 2024Article
- Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions.Diagnostics (Basel, Switzerland) · 2022Review
- Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models.Healthcare (Basel, Switzerland) · 2022Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
- The Discriminatory Ability of Ganglion Cell Inner Plexiform Layer Complex Thickness in Patients with Preperimetric Glaucoma.Journal of current ophthalmologyArticle
- Progressive Inflammatory Coupling Drives Glaucoma Progression - a Longitudinal Analysis of Cytokine-RNFL Dynamics.Romanian journal of ophthalmologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the "St. Spiridon" Hospital of Iași, Romania. Using these datasets, different classification problems were proposed. The evaluation of glaucoma progression was conducted based on parameters such as VFI (Visual field index), MD (Mean Deviation), PSD (Pattern standard deviation), and RNFL (Retinal Nerve Fiber Layer). As classification tools, the following algorithms were used: Multilayer Perceptron, Random Forest, Random Tree, C4.5, k-Nearest Neighbors, Support Vector Machine, and Non-Nested Generalized Exemplars. The best results, with an accuracy of over 90%, were obtained with Multilayer Perceptron and Random Forest algorithms. The NNGE algorithm also proved very useful in creating a hierarchy of the input values according to their influence (weight) on the considered outputs. On the other hand, the decision tree algorithms gave us insight into the logic used in their classification, which is of practical importance in obtaining additional information regarding the rationale behind a certain rule or decision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.