Application of random forests methods to diabetic retinopathy classification analyses.

ABSTRACT:

Background

Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects.

Methodology

Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance.

Principal findi

SUBMITTER: Casanova R 

PROVIDER: S-EPMC4062420 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature