Volume 2 - Issue 2, March - April 2026

Diabetic Retinopathy (DR) is a serious complication of diabetes that can lead to vision loss if not detected early. With the global prevalence of diabetes rising, there is an increasing need for efficient and scalable DR screening methods. Traditional diagnosis relies on manual examination of retinal images by ophthalmologists, which is often time-consuming, subjective, and prone to human error. Machine learning (ML) has emerged as a powerful tool in medical diagnosis, enabling automated, accurate, and early prediction of DR using retinal images and patient data. This paper explores various ML approaches for DR prediction, including supervised learning, deep learning, transfer learning, and ensemble learning techniques. The role of Python in model development is highlighted, with emphasis on key libraries such as TensorFlow, PyTorch, and Scikit-Learn. The study also examines challenges in DR prediction, including data quality, model interpretability, and ethical concerns in AI-driven healthcare. In addition, it discusses the benefits of automated DR screening, such as faster diagnosis, reduced workload for ophthalmologists, improved accessibility in remote areas, and costeffectiveness. Ethical considerations, including bias, privacy, and accountability, are also addressed. Finally, the paper outlines future directions in ML-driven ophthalmology, focusing on explainable AI (XAI), federated learning, edge AI, and integration with wearable devices for continuous eye health monitoring. With ongoing advancements in AI and medical imaging, ML-powered DR detection has the potential to transform ophthalmology, improving patient outcomes and reducing the burden on healthcare systems.

Mr.I.Gobi, Ms.Illakiya P,"Diabetic Retinopathy Prediction Using Machine Learning" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(633-636) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.