Abstract

Glaucoma is a human eye illness that causes Irish-eye injury and ultimately can lead to full blindness in patients with diabetes. Glaucoma detection is vital at an early stage to prevent full blindness. Even if early diagnosis and persistent monitoring of the diabetes patient are required, effective Glaucoma treatment is available. Many physical tests can also be done to detect Glaucoma, but they are time-consuming, including visual acuity testing, pupil dilation and optical coherence tomography. The aim of the study was to decide on glaucoma by using machine learning ensemble classifying algorithms based on data taken from diverse Irish images. It gives us the precision of which algorithm is suitable and precise for disease prediction. The Random Forest, KNearest Neighbor, neural networks, and the support vector machine are responsible for the determination to glaucoma.