Machine Learning to Determine Risk Factors for Myopia Progression in Primary School Children: The Anyang Childhood Eye Study.

ABSTRACT:

Introduction

To investigate the risk factors for myopia progression in primary school children and build prediction models by applying machine learning to longitudinal, cycloplegic autorefraction data.

Methods

A total of 2740 children from grade 1 to grade 6 were examined annually over a period of 5 years. Myopia progression was determined as change in cycloplegic autorefraction. Questionnaires were administered to gauge environmental factors. Each year, risk factors were evaluated and prediction models were built in a training group and then tested in an independent hold-out group using the random forest algorithm.

Results

Six variables appeared in prediction models on myopia progression for all 5 years, with combined weight of 77% and prediction accuracy over 80%. U

SUBMITTER: Li SM 

PROVIDER: S-EPMC8927561 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature