title: 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.
  
  
  METHODS
  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.
  
  
  RESULTS
  Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables.
  
  
  CONCLUSIONS
  We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Ramon
authors.initials: R
authors.lastname: Casanova
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Santiago
authors.initials: S
authors.lastname: Saldana
authors.affiliation: National Eye Institute, National Institutes of Health [NIH], Bethesda, Maryland, United States of America.
authors.forename: Emily Y
authors.initials: EY
authors.lastname: Chew
authors.affiliation: Fundus Photograph Reading Center, University of Wisconsin, Madison, Wisconsin, United States of America.
authors.forename: Ronald P
authors.initials: RP
authors.lastname: Danis
authors.affiliation: Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Craig M
authors.initials: CM
authors.lastname: Greven
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.
authors.forename: Walter T
authors.initials: WT
authors.lastname: Ambrosius
mesh_terms: D000368:Aged; D003663:Decision Trees; D003924:Diabetes Mellitus, Type 2 / Q000150:complications*; D003930:Diabetic Retinopathy / Q000145:classification* / Q000175:diagnosis; D003941:Diagnostic Techniques, Ophthalmological; D006801:Humans; D008875:Middle Aged; D012372:ROC Curve