Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.

ABSTRACT:

Purpose

To develop and test machine learning classifiers (MLCs) for determining visual field progression.

Methods

In total, 90,713 visual fields from 13,156 eyes were included. Six different progression algorithms (linear regression of mean deviation, linear regression of the visual field index, Advanced Glaucoma Intervention Study algorithm, Collaborative Initial Glaucoma Treatment Study algorithm, pointwise linear regression [PLR], and permutation of PLR) were applied to classify each eye as progressing or stable. Six MLCs were applied (logistic regression, random forest, extreme gradient boosting, support vector classifier, convolutional neural network, fully connected neural network) using a training and testing set. For MLC input, visual fields for a given eye were divi

SUBMITTER: Saeedi O 

PROVIDER: S-EPMC8237084 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature