Applying "Lasso" Regression to Predict Future Glaucomatous Visual Field Progression in the Central 10 Degrees.
Summary
Mean deviation prediction using OLSLR with a small number of VFs resulted in large prediction errors. It was useful to apply Lasso regression when predicting future progression of the central 10 degrees, compared to OLSLR.
Abstract
PURPOSE OF THE STUDY
We recently reported that it is beneficial to apply least absolute shrinkage and selection operator (Lasso) regression to predict future 24-2 visual field (VF) progression. The purpose of the current study was to investigate the usefulness of Lasso regression to predict VF progression in the central 10 degrees (10-2) in glaucoma patients.
METHODS
Series of 10 VFs (Humphrey Field Analyzer 10-2 SITA-standard) from each of 149 eyes in 110 open angle glaucoma patients, obtained over 5.7±1.4 years (mean±SD) were investigated. Mean deviation values of the 10th VF were predicted using varying numbers of VFs (ranging from the first to third VFs to the first to ninth VFs), applying ordinary least square regression (OLSLR) and Lasso regression. Absolute prediction errors were then compared.
RESULTS
With OLSLR, prediction error varied between 5.4±5.0 (using first to third VFs) and 1.1±1.6 dB (using first to ninth VFs). Significantly smaller prediction errors were obtained with Lasso regression, in particular with small numbers of VFs (from 2.1±2.8: first to third VFs, to 1.0±1.6 dB: first to ninth VFs). A large λ value, which is an index showing the degree of penalty in Lasso regression, was observed when a small number of VFs were used for prediction.
CONCLUSION
Mean deviation prediction using OLSLR with a small number of VFs resulted in large prediction errors. It was useful to apply Lasso regression when predicting future progression of the central 10 degrees, compared to OLSLR.
More by Yuri Fujino
View full profile →Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.
Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.
Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.
Top Research in Visual Field
Browse all →Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications.
Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma.
Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT).
Discussion
Comments and discussion will appear here in a future update.