Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling

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...

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Autores: Santos, Luis F. F. M., Sánchez Tena, Miguel Ángel, Álvarez Peregrina, Cristina, Martínez Pérez, Clara
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/125768
Acesso em linha:https://hdl.handle.net/20.500.14352/125768
Access Level:acceso abierto
Palavra-chave:004.8
611.84
Machine learning
Artificial intelligence
Assisted diagnosis
Data labeling
Knowledge engineering
Optometría
2209 Óptica
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spelling Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive ModelingSantos, Luis F. F. M.Sánchez Tena, Miguel ÁngelÁlvarez Peregrina, CristinaMartínez Pérez, Clara004.8611.84Machine learningArtificial intelligenceAssisted diagnosisData labelingKnowledge engineeringOptometría2209 ÓpticaArtificial 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.MDPIUniversidad Complutense de Madrid20252025-10-0120252025-10-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/125768reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1257682026-06-02T12:44:21Z
dc.title.none.fl_str_mv Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
title Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
spellingShingle Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
Santos, Luis F. F. M.
004.8
611.84
Machine learning
Artificial intelligence
Assisted diagnosis
Data labeling
Knowledge engineering
Optometría
2209 Óptica
title_short Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
title_full Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
title_fullStr Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
title_full_unstemmed Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
title_sort Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling
dc.creator.none.fl_str_mv Santos, Luis F. F. M.
Sánchez Tena, Miguel Ángel
Álvarez Peregrina, Cristina
Martínez Pérez, Clara
author Santos, Luis F. F. M.
author_facet Santos, Luis F. F. M.
Sánchez Tena, Miguel Ángel
Álvarez Peregrina, Cristina
Martínez Pérez, Clara
author_role author
author2 Sánchez Tena, Miguel Ángel
Álvarez Peregrina, Cristina
Martínez Pérez, Clara
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 004.8
611.84
Machine learning
Artificial intelligence
Assisted diagnosis
Data labeling
Knowledge engineering
Optometría
2209 Óptica
topic 004.8
611.84
Machine learning
Artificial intelligence
Assisted diagnosis
Data labeling
Knowledge engineering
Optometría
2209 Óptica
description 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-10-01
2025
2025-10-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/125768
url https://hdl.handle.net/20.500.14352/125768
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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