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Abstract #57148 Published in IGR 16-2
Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma
Hirasawa H; Murata H; Mayama C; Araie M; Asaoka RBritish Journal of Ophthalmology 2014; 98: 1230-1235
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.
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Classification:
1.4 Quality of life (Part of: 1 General aspects)
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
