Predicting Children's Myopia Risk : A Monte Carlo Approach to Compare the Performance of Machine Learning Models
Artiemjew, 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.), ICAART 2024 : Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Volume 3 (pp. 1092-1099). SCITEPRESS Science and Technology Publications. https://doi.org/10.5220/0012435500003636
Tekijät
Päivämäärä
2024Tekijänoikeudet
© 2024 SCITEPRESS
This 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.
Julkaisija
SCITEPRESS Science and Technology PublicationsEmojulkaisun ISBN
978-989-758-680-4Konferenssi
International Conference on Agents and Artificial IntelligenceKuuluu julkaisuun
ICAART 2024 : Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Volume 3ISSN Hae Julkaisufoorumista
2184-433XAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/207561430
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
Shahroud School Children Eye Cohort Study is funded by the Noor Ophthalmology Research Center and Shahroud University of Medical Sciences. (Grant numbers: 9329, 960351).Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Effect of variable selection strategy on the predictive models for adverse pregnancy outcomes of pre-eclampsia : A retrospective study
Zheng, Dongying; Hao, Xinyu; Khan, Muhanmmad; Kang, Fuli; Li, Fan; Hämäläinen, Timo; Wang, Lixia (Scholar Media Publishing Company, 2024)Objectives: The improvement of prediction for adverse pregnancy outcomes is quite essential to the women suffering from pre-eclampsia, while the collection of predictive indicators is the prerequisite. The traditional ... -
Associations of Children’s Close Reading Distance and Time Spent Indoors with Myopia, Based on Parental Questionnaire
Pärssinen, Olavi; Lassila, Essi; Kauppinen, Markku (MDPI AG, 2022)Purpose: To study the association of parents’ reports about their children’s near work and outdoor habits with myopia in their children. Methods: Data from a questionnaire study conducted in 1983 among Finnish schoolchildren ... -
Comparing the forecasting performance of logistic regression and random forest models in criminal recidivism
Aaltonen, Olli-Pekka (2016)Rikosseuraamusalalla on viime vuosina kehitetty uusintarikollisuutta ennustavia malleja (Tyni, 2015), jotka perustuvat tyypillisesti rekisteripohjaisiin mittareihin, jotka mittaavat mm. tuomitun sukupuolta, ikää, rikostaustaa ... -
Dynamic integration of classifiers for handling concept drift
Tsymbal, Alexey; Pechenizkiy, Mykola; Cunningham, Padraig; Puuronen, Seppo (Elsevier, 2008)In the real world concepts are often not stable but change with time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as new pathogen strains ... -
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia : A retrospective study
Zheng, Dongying; Hao, Xinyu; Khan, Muhanmmad; Wang, Lixia; Li, Fan; Xiang, Ning; Kang, Fuli; Hamalainen, Timo; Cong, Fengyu; Song, Kedong; Qiao, Chong (Frontiers Media SA, 2022)Introduction: Preeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.