Machine Learning automatic assessment for glaucoma and myopia based on Corvis ST data

Abstract

Glaucoma is a silent disease characterized by progressive degeneration of retinal ganglion cells and, when not detected or treated early, can lead to blindness. Computer systems have demonstrated their efficiency in the medical decision-making process and Artificial Intelligence (AI) techniques have helped advances in ophthalmology, allowing for faster and more effective detection of glaucoma. Machine learning is a very promising subfield of AI that supports research in understanding the development, progression and treatment of glaucoma, identifying new risk factors and assessing the importance of existing ones.This study aims to test and analyze the results of different models of supervised machine learning in the detection and classification of ophthalmic diseases (Glaucoma, high myopia and low myopia) based on data from Corvis ST. The most important characteristics were selected based on a variance greater than 0.02. In terms of accuracy, the models that obtained the best results were Random Forrest 0.73, Stochastic Gradient Descent (SGD) 0.75, Gradient Boosting Classifier (GBC) 0.76 and K-Nearest Neighbors 0.71. The GBC model achieved the best results in accuracy, AUC, Recall and F1Score 76.00, 52.5, 78.00, 70.2 respectively.