Predicting Children's Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models

Piotr Artiemjew, Radosław Cybulski, Mohammad Hassan Emamian, Andrzej Grzybowski, Andrzej Jankowski, Carla Lanca, Carla Lanca, Shiva Mehravaran, Marcin Młyński, Cezary Morawski, Klaus Nordhausen, Olavi Pärssinen, Krzysztof Ropiak

2024

Abstract

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.

Paper Citation

in Harvard Style

Artiemjew P., Cybulski R., Emamian M., Grzybowski A., Jankowski A., Lanca C., Mehravaran S., Młyński M., Morawski C., Nordhausen K., Pärssinen O. and Ropiak K. (2024). 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 - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1092-1099. DOI: 10.5220/0012435500003636

in Bibtex Style

@conference{icaart24,
author={Piotr Artiemjew and Radosław Cybulski and Mohammad Hassan Emamian and Andrzej Grzybowski and Andrzej Jankowski and Carla Lanca and Shiva Mehravaran and Marcin Młyński and Cezary Morawski and Klaus Nordhausen and Olavi Pärssinen and Krzysztof Ropiak},
title={Predicting Children's Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1092-1099},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012435500003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Predicting Children's Myopia Risk: A Monte Carlo Approach to Compare the Performance of Machine Learning Models
SN - 978-989-758-680-4
AU - Artiemjew P.
AU - Cybulski R.
AU - Emamian M.
AU - Grzybowski A.
AU - Jankowski A.
AU - Lanca C.
AU - Mehravaran S.
AU - Młyński M.
AU - Morawski C.
AU - Nordhausen K.
AU - Pärssinen O.
AU - Ropiak K.
PY - 2024
SP - 1092
EP - 1099
DO - 10.5220/0012435500003636
PB - SciTePress