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dc.contributor.authorArtiemjew, Piotr
dc.contributor.authorCybulski, Radosław
dc.contributor.authorEmamian, Mohammad
dc.contributor.authorGrzybowski, Andrzej
dc.contributor.authorJankowski, Andrzej
dc.contributor.authorLanca, 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.contributor.editorRocha, Ana Paula
dc.contributor.editorSteels, Luc
dc.contributor.editorHerik, Jaap van den
dc.date.accessioned2024-03-14T12:22:14Z
dc.date.available2024-03-14T12:22:14Z
dc.date.issued2024
dc.identifier.citationArtiemjew, P., Cybulski, R., Emamian, M., Grzybowski, A., Jankowski, A., Lanca, C., Mehravaran, S., Młyński, M., Morawski, C., Nordhausen, K., Pärssinen, O., & Ropiak, K. (2024). Predicting Children's Myopia Risk : A Monte Carlo Approach to Compare the Performance of Machine Learning Models. In A. P. Rocha, L. Steels, & J. V. D. Herik (Eds.), <i>ICAART 2024 : Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Volume 3</i> (pp. 1092-1099). SCITEPRESS Science and Technology Publications. <a href="https://doi.org/10.5220/0012435500003636" target="_blank">https://doi.org/10.5220/0012435500003636</a>
dc.identifier.otherCONVID_207561430
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/93902
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 (Natural 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.en
dc.format.extent1443
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSCITEPRESS Science and Technology Publications
dc.relation.ispartofICAART 2024 : Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Volume 3
dc.rightsCC BY-NC-ND 4.0
dc.subject.othermyopia prediction
dc.subject.othermachine learning
dc.subject.otherdata analysis
dc.subject.otherMonte Carlo simulations
dc.subject.otherLasso regression
dc.titlePredicting Children's Myopia Risk : A Monte Carlo Approach to Compare the Performance of Machine Learning Models
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202403142416
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosLiikuntatieteellinen tiedekuntafi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.laitosFaculty of Sport and Health Sciencesen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-989-758-680-4
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1092-1099
dc.relation.issn2184-433X
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 SCITEPRESS
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Agents and Artificial Intelligence
dc.subject.ysoMonte Carlo -menetelmät
dc.subject.ysokoneoppiminen
dc.subject.ysolapset (ikäryhmät)
dc.subject.ysolikinäköisyys
dc.subject.ysomallit (mallintaminen)
dc.subject.ysoriskitekijät
dc.subject.ysoennusteet
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p6361
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p4354
jyx.subject.urihttp://www.yso.fi/onto/yso/p5995
jyx.subject.urihttp://www.yso.fi/onto/yso/p510
jyx.subject.urihttp://www.yso.fi/onto/yso/p13277
jyx.subject.urihttp://www.yso.fi/onto/yso/p3297
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.5220/0012435500003636
jyx.fundinginformationShahroud School Children Eye Cohort Study is funded by the Noor Ophthalmology Research Center and Shahroud University of Medical Sciences. (Grant numbers: 9329, 960351).
dc.type.okmA4


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