Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007–2008

TL;DR

This study uses machine learning to predict age-related macular degeneration based on pesticide exposure data from a national health survey.

Contribution

The novel contribution is the application of machine learning models, particularly random forest, to analyze AMD risk linked to pesticide exposure.

Findings

The random forest model achieved a ROC value of 0.75 in predicting AMD.

SHAP analysis identified chlorpyrifos and malathion as significant contributors to AMD.

Pesticide exposure data from NHANES was effectively used to build and validate the model.

Abstract

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.

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