Data-Driven Insights for Diabetic Retinopathy Detection: The Role of Deep Learning in Predictive Healthcare

This article explores the application of deep learning methods in predictive analytics in the healthcare industry, namely prediction and early diagnosis of diabetic retinopathy (DR). Since more than 470 million people are suffering from diabetes all over the world, early detection and treatment of diabetes complications are of the highest importance. This study compares different deep learning models applied in medical databases, i.e., patient medical history, to identify which models show the most level of effectiveness in predicting diabetic retinopathy. Image processing methods such as optic disc detection, blood vessel extraction, and exudate detection are employed by the study to identify diabetes-related abnormalities in retinal images. The article emphasizes the importance of early detection and the promise of non-invasive diagnosis to enhance diabetes control and prevent permanent blindness. In addition, the study emphasizes the application of big data analytics to enable medical professionals to have timely, data-driven insights that potentially could have a life-saving effect.