Machine learning-based prediction of diabetic retinopathy from pupillary abnormalities in a South Indian population
Fig 3
Top pupillometry features identified by the Light Gradient Boosting Machine (LightGBM) importance scores for disease classification.
The features include demographic factors such as age, gender and pupillary dynamics such as Hippus cycle time, dark-adapted baseline diameter, constriction and re-dilatation amplitudes, their respective times (tâ, tâ), and velocities of constriction and dilatation (mm/s).