Abstract
PurposeThe purpose of this study was to create a vision-related quality of life (VRQoL) prediction system to identify visual field (VF) test points associated with decreased VRQoL in patients with glaucoma.MethodVRQoL score was surveyed in 164 patients with glaucoma using the ‘Sumi questionnaire’. A binocular VF was created from monocular VFs by using the integrated VF (IVF) method. VRQoL score was predicted using the ‘Random Forest’ method, based on visual acuity (VA) of better and worse eyes (better-eye and worse-eye VA) and total deviation (TD) values from the IVF. For comparison, VRQoL scores were regressed (linear regression) against: (i) mean of TD (IVF MD); (ii) better-eye VA; (iii) worse-eye VA; and (iv) IVF MD and better- and worse-eye VAs. The rank of importance of IVF test points was identified using the Random Forest method.ResultsThe root mean of squared prediction error associated with the Random Forest method (0.30 to 1.97) was significantly smaller than those with linear regression models (0.34 to 3.38, p<0.05, ten-fold cross validation test). Worse-eye VA was the most important variable in all VRQoL tasks. In general, important VF test points were concentrated along the horizontal meridian. Particular areas of the IVF were important for different tasks: peripheral superior and inferior areas in the left hemifield for the ‘letters and sentences’ task, peripheral, mid-peripheral and para-central inferior regions for the ‘walking’ task, the peripheral superior region for the ‘going out’ task, and a broad scattered area across the IVF for the ‘dining’ task.ConclusionThe VRQoL prediction model with the Random Forest method enables clinicians to better understand patients’ VRQoL based on standard clinical measurements of VA and VF.
Highlights
Vision-related quality of life (VRQoL) can be defined as a person’s satisfaction with their visual ability and how their vision impacts on their daily life [1]
The root mean of squared prediction error associated with the Random Forest method (0.30 to 1.97) was significantly smaller than those with linear regression models (0.34 to 3.38, p,0.05, ten-fold cross validation test)
Visual field (VF) loss [2,3,4,5,6,7,8,9,10,11] and reduced visual acuity (VA) [8,9,10,11,12,13,14] impact on VRQoL; these studies only investigated the influence of summary measures, such as mean deviation (MD), on VRQoL
Summary
Vision-related quality of life (VRQoL) can be defined as a person’s satisfaction with their visual ability and how their vision impacts on their daily life [1]. The two most significant measures of visual function, VA and VF sensitivity, are correlated in glaucoma patients [7], especially when glaucomatous damage affects the central VF [25,26]. VF sensitivities of neighboring test points are correlated [27,28,29]; this spatial relationship should be taken into account when analyzing the relationship between the VF and VRQoL. We have used Breiman’s ‘Random Forest’ machine learning algorithm [30] to predict VRQoL, and to identify the most important VF test points for a number of different daily tasks since this method can cope with highly correlated predictor variables. The Random Forest algorithm has be used to explore interactions between different predictor variables[31,32,33]; VA and VF sensitivity can be considered concurrently, and the spatial relationship between neighboring VF test points will not bias the results
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