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Predicting children’s myopia risk: a Monte Carlo approach to compare the performance of machine learning models

2024-02Journal article Open access
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dc.contributor.authorArtiemjew, Piotr
dc.contributor.authorCybulski, Radosław
dc.contributor.authorEmamian, Mohammad Hassan
dc.contributor.authorGrzybowski, Andrzej
dc.contributor.authorJankowski, Andrzej
dc.contributor.authorLança, Carla
dc.contributor.authorMehravaran, Shiva
dc.contributor.authorMłyński, Marcin
dc.contributor.authorMorawski, Cezary
dc.contributor.authorNordhausen, Klaus
dc.contributor.authorPärssinen, Olavi
dc.contributor.authorRopiak, Krzysztof
dc.date.accessioned2024-05-06T09:57:42Z
dc.date.available2024-05-06T09:57:42Z
dc.date.issued2024-02
dc.description.abstractThis study presents the initial results of the Myopia Risk Calculator (MRC) Consortium, introducing an innovative approach to predict myopia risk by using trustworthy machine-learning models. The dataset included approximately 7,945 records (eyes) from 3,989 children. We developed a myopia risk calculator and an accompanying web interface. Central to our research is the challenge of model trustworthiness, specifically evaluating the effectiveness and robustness of AI (Artificial Intelligence)/ML (Machine Learning)/NLP (Nat-ural Language Processing) models. We adopted a robust methodology combining Monte Carlo simulations with cross-validation techniques to assess model performance. Our experiments revealed that an ensemble of classifiers and regression models with Lasso regression techniques provided the best outcomes for predicting myopia risk. Future research aims to enhance model accuracy by integrating image and synthetic data, including advanced Monte Carlo simulations.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationArtiemjew P, Cybulski R, Emamian M, Grzybowski A, Jankowski A, Lança C, et al. Predicting children’s myopia risk: a Monte Carlo approach to compare the performance of machine learning models. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence – ICAART, Rome (Italy), February 24-26, 2024. Vol. 3, p. 1092-9.pt_PT
dc.identifier.doi10.5220/0012435500003636pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/17428
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherScite Presspt_PT
dc.relation.publisherversionhttps://www.scitepress.org/Link.aspx?doi=10.5220/0012435500003636pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectOphthalmologypt_PT
dc.subjectMyopiapt_PT
dc.subjectMyopia predictionpt_PT
dc.subjectData analysispt_PT
dc.subjectLasso regressionpt_PT
dc.subjectMachine learningpt_PT
dc.subjectMonte Carlo simulationspt_PT
dc.titlePredicting children’s myopia risk: a Monte Carlo approach to compare the performance of machine learning modelspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1099pt_PT
oaire.citation.startPage1092pt_PT
oaire.citation.volume3pt_PT
person.familyNameLança
person.givenNameCarla
person.identifier.ciencia-id601A-6412-BF2F
person.identifier.orcid0000-0001-9918-787X
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0320b455-ee19-4670-8bf2-10dce9de1bec
relation.isAuthorOfPublication.latestForDiscovery0320b455-ee19-4670-8bf2-10dce9de1bec

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