• MUH. ERDIN
• Prof. Lalitkumar Patel

Automated eye disease evaluations will assist ophthalmologists in screening patients. Ophthalmologists will be able to detect eye diseases with increased speed, accuracy, and reliability due to machine learning. Through rapid identification, treatment can be managed by health & human services. The types of Machine Learning utilized in the experiment are SVM, KNN, Logical Regression, Random Forest, and Decision Tree. The same data set and feature selection are utilized to develop all five models. The results show that SVM produces better performance than other models with 85.57% accuracy, 86% precision, 86% recall, and an 84% F-1 score. So that SVM can be used to test for eye disease, image input is provided to the model, which is then classified according to its class.

KNN, SVM, Decision Tree, Random Forest, Logistic Regression, Machine Learning

"Eye Disease Detection Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.g181-g187, April-2023, Available :http://www.jetir.org/papers/JETIR2304829.pdf

"Eye Disease Detection Using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppg181-g187, April-2023, Available at : http://www.jetir.org/papers/JETIR2304829.pdf