title: Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma.
abstract: OBJECTIVE
  We investigated whether it was useful to use machine learning algorithms to predict patients' vision related quality of life (VRQoL) from visual field (VF) and visual acuity (VA).
  
  
  METHODS
  VRQoL was surveyed in 164 glaucomatous patients using the Sumi questionnaire. Their VRQoL score was predicted using machine learning algorithms (Random Forest, gradient boosting, support vector machine) based on total deviation (TD) values from integrated VF (IVF), VA, age and gender. For comparison, VRQoL score was predicted using standard linear regression with mean of IVF, TD values, and VA, and also the stepwise model selection by Akaike Information Criterion. Prediction error was calculated as root mean of the squared prediction error (RMSE) associated with the leave one out cross validation.
  
  
  RESULTS
  RMSEs associated with general VRQoL score were smaller for the machine learning algorithms (1.99 to 2.21) compared with the standard linear model and the stepwise model selection (2.35 to 3.20). A similar tendency was found in each individual VRQoL task score.
  
  
  CONCLUSIONS
  We found that it was advantageous to use machine learning methods to predict VRQoL accurately. These statistical methods could be used to help clinicians better understand patients' VRQoL without the need for extra tests other than standard VA and VF.
authors.affiliation: Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
authors.forename: Hiroyo
authors.initials: H
authors.lastname: Hirasawa
authors.affiliation: Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Murata
authors.affiliation: Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
authors.forename: Chihiro
authors.initials: C
authors.lastname: Mayama
authors.affiliation: Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, Tokyo, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Araie
authors.affiliation: Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
authors.forename: Ryo
authors.initials: R
authors.lastname: Asaoka
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D005260:Female; D005901:Glaucoma / Q000503:physiopathology / Q000534:rehabilitation*; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D011594:Psychometrics; D011788:Quality of Life; D014792:Visual Acuity / Q000502:physiology*; D058609:Visual Field Tests / Q000379:methods; D014794:Visual Fields / Q000502:physiology*
keywords: Glaucoma; Quality of Life; Visual Field