Table 1.
Optimized hyperparameters used in various machine learning models.
Table 2.
Demographic of patients with visual field data*.
Fig 1.
Density plot of rate of MD change.
Top panel shows MD slope for all eyes (green). Bottom two panels are subdivided into rates of change for rapid (red) and non-rapid (blue) progressors. Dashed lines indicate mean values.
Fig 2.
Density plots of rapid (red) and non-rapid (blue) progressors by age, Mean Deviation (MD), Pattern Standard Deviation (PSD) and foveal threshold at baseline. Dashed lines indicate mean values for the independent variables in rapid (red) and non-rapid (blue) progressors.
Fig 3.
Density plots of rapid (red) and non-rapid (blue) progressors by VF reliability indices (false negative responses, false positive responses and fixation losses) at baseline. Dashed lines indicate mean values for the independent variables in rapid (red) and non-rapid (blue) progressors.
Fig 4.
Receiver operating curves that demonstrate the ability of various machine learning methods to classify rapid and non-rapid progressors based on the results of the first or first two visual fields.
The colors represent the following machine learning methods; Green- support vector machine; Blue- artificial neural network; Purple- random forest; Orange—logistic regression; Red—naïve Bayes. Black–Hybrid model. The solid lines represent models trained on data from the first VF alone and the dashed line represents models trained on data from the first and second VF. The area under the curve and 95% CI are shown on the bottom right (the top number of each color is model trained on first VF while the bottom number is models trained on first two VFs).
Table 3.
Model performance metrics.
Fig 5.
Breiman-Cutler variable importance metrics of the inputs for the random forest model.
Table 4.
Factors that influence the odds of being labeled a rapid progressor based on output from the logistic regression model.