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ORIGINAL RESEARCH article
Front. Public Health
Sec. Environmental Health and Exposome
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1561913
Machine learning model for age related macular degeneration based on pesticides: The National Health and Nutrition Examination Survey 2007 to 2008
Provisionally accepted- First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Age-related macular degeneration (AMD) is the most common cause of irreversible deterioration of vision in older adults. Previous studies have found that exposure to pesticides can lead to a worsening of AMD. In this paper, information on pesticide exposure and AMD from the National Health and Nutrition Examination Survey (NHANES) database was used to divide the data into a training set and a validation set. Firstly, the correlation between the variables in the model is analyzed. The model is then built using nine machine learning algorithms and verified on a validation set. Finally, it is found that the random forest model has high predictive value, and its Receiver Operating Characteristic (ROC) value is 0.75. Finally, SHapley additive interpretation (SHAP) analysis was used to rank the importance of each variable in the random forest model, and it was found that chlorpyrifos and malathion had quite significant effects on the occurrence and development of AMD.
Keywords: age related macular degeneration1, Pesticides2, Machine Learning3, NHANES4, cross-section study5
Received: 16 Jan 2025; Accepted: 24 Mar 2025.
Copyright: © 2025 Liu, wang and li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jiankang Liu, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
qiuming li, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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