RT Journal Article
T1 Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
A1 Santos, Luis F. F. M.
A1 Sánchez Tena, Miguel Ángel
A1 Álvarez Peregrina, Cristina
A1 Martínez Pérez, Clara
AB Artificial intelligence and machine learning have increasingly transformed optometry, enabling automated classification and predictive modeling of eye conditions. In this study, we introduce Optometry Random Forest, an artificial intelligence-based system for automated classification and forecasting of optometric data. The proposed methodology leverages Random Forest models, trained on academic optometric datasets, to classify key diagnostic categories, including Contactology, Dry Eye, Low Vision, Myopia, Pediatrics, and Refractive Surgery. Additionally, an autoRegressive integrated moving average based forecasting model is incorporated to predict future research trends in optometry until 2030. Comparing the one-shot and epoch-trained Optometry Random Forest, the findings indicate that the epoch-trained model consistently outperforms the one-shot model, achieving superior classification accuracy (97.17%), precision (97.28%), and specificity (100%). Moreover, the comparative analysis with Optometry Bidirectional Encoder Representations from Transformers demonstrates that the Optometry Random Forest excels in classification reliability and predictive analytics, positioning it as a robust artificial intelligence tool for clinical decision-making and resource allocation. This research highlights the potential of Random Forest models in medical artificial intelligence, offering a scalable and interpretable solution for automated diagnosis, predictive analytics, and artificial intelligence-enhanced decision support in optometry. Future work should focus on integrating real-world clinical datasets to further refine classification performance and enhance the potential for artificial intelligence-driven patient care.
PB MDPI
YR 2025
FD 2025-10
LK https://hdl.handle.net/20.500.14352/125768
UL https://hdl.handle.net/20.500.14352/125768
LA eng
NO Santos, L.F.F.M.; Sánchez-Tena, M.Á.; Alvarez-Peregrina, C.; Martinez-Perez, C. Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling. Algorithms 2025, 18, 647. https://doi.org/10.3390/a18100647
NO Fundação para a Ciência e Tecnologia
NO Aeronautics and Astronautics Research Center (AEROG)
NO Laboratório Associado em Energia, Transportes e Aeroespacial (LAETA)
DS Docta Complutense
RD 17 mar 2026