Contributors of Myopia using Major First Order Satisfiability Reverse Analysis

DOI

10.2991/978-94-6239-745-3_29How to use a DOI?

Keywords

Data Mining; Satisfiability; Reverse Analysis; Artificial Intelligence; Neural Networks

Abstract

Myopia is a growing concern for vision health, particularly among children and adolescents. The growing prevalence of myopia calls for a systematic approach to identify the key contributing factors behind its progression. This study presents an artificial intelligence with logic mining technique to identify the major contributors to myopia using the Major First Order Satisfiability Reverse Analysis (MFOSRA) model. The model incorporates several core components, including a Discrete Hopfield Neural Network, Exhaustive Search Algorithm as the training algorithm, Major First Order Satisfiability as the logical rule structure, a feature selection method based on the Pearson Correlation and a Reverse Analysis approach. The experiments were carried out using a health dataset related to myopia that included genetic, environmental and lifestyle indicators. The MFOSRA model is capable of extracting optimal logic patterns from the dataset in the form of interpretable rules. The induced logic highlights key contributors to myopia such as screen exposure time, lack of outdoor activities and family history. The results show that the proposed method providing high level of accuracy (91.13%). This demonstrates that the logic mining approach can extract optimum patterns from a dataset in the form of best induced logic. The resulting logic consists of composite measures of near-work activities, spherical equivalent refraction, parental myopia status, anterior chamber depth, the year the subject entered the study, time spent in sports activities and reading for pleasure. These findings indicate that this approach valuable for health researchers, patient guardians and medical professionals.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Nurul Huda Ahmad Rusli
AU  - Nurin Kamalin Mohd Fadil
AU  - Nurin Hazwani Hamidi
AU  - Farisya Husna Mansor
AU  - Muhammad Ameer Azammudin
AU  - Nur Ezlin Zamri
PY  - 2026
DA  - 2026/08/24
TI  - Contributors of Myopia using Major First Order Satisfiability Reverse Analysis
BT  - Proceedings of the International Conference on Research, Innovation, Sustainability, and Educations (IRISECON 2026)
PB  - Atlantis Press
SP  - 431
EP  - 484
SN  - 3091-4442
UR  - https://doi.org/10.2991/978-94-6239-745-3_29
DO  - 10.2991/978-94-6239-745-3_29
ID  - Rusli2026
ER  -