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

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 (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.

Keywords

Ophthalmology Myopia Myopia prediction Data analysis Lasso regression Machine learning Monte Carlo simulations

URI

http://hdl.handle.net/10400.21/17428

Citation

Artiemjew 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.