title: Development of predictive models of proliferative vitreoretinopathy based on genetic variables: the Retina 4 project.
abstract: OBJECTIVE
  Machine learning techniques were used to identify which of 14 algorithms best predicts the genetic risk for development of proliferative vitreoretinopathy (PVR) in patients who are experiencing primary rhegmatogenous retinal detachment (RD).
  
  
  METHODS
  Data from a total of 196 single nucleotide polymorphisms in 30 candidate genes were used. The genotypic profile of 138 patients with PVR following primary rhegmatogenous RD and 312 patients without PVR RD were analyzed. Machine learning techniques were used to develop statistical predictive models. Fourteen models were assessed. Their reproducibility was evaluated by an internal cross-validation method.
  
  
  RESULTS
  The three best predictive models were the lineal kernel based on the Support Vector Machine (SMV), the radial kernel based on the SVM, and the Random Forest. Accuracy values were 78.4%, 70.3%, and 69.3%, respectively. The more accurate, although complex, algorithm uses 42 SNPs, whereas the simpler one uses only two SNPs, which makes them more suitable for routine diagnostic work. The radial kernel based on SVM uses 10 SNPs. The best individually predictor marker was rs2229094 in the tumor necrosis factor locus.
  
  
  CONCLUSIONS
  Genetic variables may be useful to predict the likelihood of the development of PVR. The predictive capabilities of these models are as good as those observed with clinical approaches. These results need external validation to estimate the true predictive capability and select the most appropriate ones for clinical use.
authors.affiliation: Department of Ophthalmology, University of Valladolid, IOBA (Institute for Research in Ophthalmobiology), Valladolid, Spain. jimena@ioba.med.uva.es
authors.forename: Jimena
authors.initials: J
authors.lastname: Rojas
authors.forename: Itziar
authors.initials: I
authors.lastname: Fernandez
authors.forename: J Carlos
authors.initials: JC
authors.lastname: Pastor
authors.forename: Maria-Teresa
authors.initials: MT
authors.lastname: Garcia-Gutierrez
authors.forename: Rosa-Maria
authors.initials: RM
authors.lastname: Sanabria
authors.forename: Maria
authors.initials: M
authors.lastname: Brion
authors.forename: Beatriz
authors.initials: B
authors.lastname: Sobrino
authors.forename: Lucia
authors.initials: L
authors.lastname: Manzanas
authors.forename: Antonio
authors.initials: A
authors.lastname: Giraldo
authors.forename: Enrique
authors.initials: E
authors.lastname: Rodriguez-de la Rua
authors.forename: Angel
authors.initials: A
authors.lastname: Carracedo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D005136:Eye Proteins / Q000235:genetics; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D012163:Retinal Detachment / Q000150:complications / Q000235:genetics*; D018630:Vitreoretinopathy, Proliferative / Q000209:etiology / Q000235:genetics*
chemical_list: D005136:Eye Proteins; D005819:Genetic Markers