Julie Trinh

Document Type

Honors Thesis


Progressive myopia resulting from axial elongation contributes to a greater risk for a wide variety of ocular conditions, such as retinal detachment and myopic macular degeneration. High myopia is especially a concern for children, who are more susceptible to the development of ocular diseases due to an early onset of myopia. To further understand myopia progression, a random forest analysis was performed on a subset of data from the Orinda Longitudinal Study of Myopia to determine what factors are the best predictors of myopia development in children. Random forests function by utilizing bootstrapped data and machine-learning algorithms to create a series of decision trees that compare the predictive ability of different variables. The starting spherical equivalent was found to be the greatest predictor of whether or not children in the study became myopic. This highlights the importance of early eye exams for children, even before corrective lenses are needed.

Publication Date






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