Predictive Analytics for Diabetes and Diabetic Retinopathy Using Deep Learning on Fundus Images

Diabetes is a chronic metabolic disorder that poses a big health risk for people around the world and due to diabetes people can also develop some complications like diabetic retinopathy (DR) which causes vision impairment in some cases of diabetes. To predict the likelihood of individuals developing Diabetes and their subsequent risk of DR using fundus images we can use deep learning algorithms which include Convolutional Neural Networks (CNN) and K-Nearest Neural Networks (KNN) which may have potential. By using extensive training for a dataset with fundus images and clinical records, the deep learning model tries to detect DR in diabetic patients to detect retinal abnormalities. This is a non-invasive and cost-effective method for early detection of DR as it analysis retinal features for risk assessment which may transform diabetic screening programs. We can facilitate timely interventions by enabling personalised risk evaluation, by this study we mitigate the vision loss associated with diabetes. We can encourage new ways to treat these diseases by understanding how retinal features are connected. It can change how we diagnose and prevent diabetes and the problems it causes and improve how we take care of patients and make them healthier.