International Research Journal of Education and Technology

Diabetic Retinopathy (DR) is a serious complication of diabetes that impacts the eyes and can lead to vision impairment if not identified early. With the rising global prevalence of diabetes, there is an urgent need for efficient and scalable DR screening methods. Traditional diagnostic approaches, which rely on manual examination of retinal images, are often time- consuming, subjective, and susceptible to human error. Machine learning (ML) has emerged as a transformative tool in medical diagnostics, offering automated, accurate, and early prediction of DR using retinal images and patient data. This paper explores various ML techniques for DR prediction, including supervised learning, deep learning, transfer learning, and ensemble learning. It also highlights the role of Python in model development, emphasizing key libraries such as TensorFlow, PyTorch, and Scikit-Learn. The study addresses challenges in DR prediction, such as data quality, model interpretability, and ethical concerns in medical AI. Additionally, it underscores the benefits of automated DR screening, including faster diagnosis, reduced workload for ophthalmologists, improved accessibility in remote areas, and cost-effectiveness. Ethical considerations, such as bias, privacy, and accountability in AI- driven healthcare, are also discussed. The paper concludes with 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 revolutionize ophthalmology, improving patient outcomes and alleviating the burden on healthcare systems.

Keywords: Diabetic Retinopathy, Machine Learning, Deep Learning, Python, Medical Imaging, AI in Healthcare, Ophthalmology.