Hiroyo Hirasawa1, Hiroshi Murata1, Chihiro Mayama1, Makoto Araie2, Ryo Asaoka1. 1. Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan. 2. Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, Tokyo, Japan.
Abstract
BACKGROUND/AIMS: 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
BACKGROUND/AIMS: 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 glaucomatouspatients 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Authors: Fatemeh Seyednasrollah; Johanna Mäkelä; Niina Pitkänen; Markus Juonala; Nina Hutri-Kähönen; Terho Lehtimäki; Jorma Viikari; Tanika Kelly; Changwei Li; Lydia Bazzano; Laura L Elo; Olli T Raitakari Journal: Circ Cardiovasc Genet Date: 2017-06
Authors: Brian C Stagg; Joshua D Stein; Felipe A Medeiros; Barbara Wirostko; Alan Crandall; M Elizabeth Hartnett; Mollie Cummins; Alan Morris; Rachel Hess; Kensaku Kawamoto Journal: Ophthalmol Glaucoma Date: 2020-08-15
Authors: Nita G Valikodath; Tala Al-Khaled; Emily Cole; Daniel S W Ting; Elmer Y Tu; J Peter Campbell; Michael F Chiang; Joelle A Hallak; R V Paul Chan Journal: J AAPOS Date: 2021-06-01 Impact factor: 1.325