OCULAR PROGNOSIS USING MACHINE LEARNING

Computer Engineering

Diabetes mellitus,a chronic metabolic disorder,poses a significant global health challenge. Early detection and proactive management of diabetes can significantly mitigate its impact on individuals. This research introduces eye lens, a novel predictive model that leverages ocular features extracted from retinal images for early diabetic prediction. By employing advanced machine learning algorithms, eye lens aims to offer an accessible and non-invasive solution to identify individuals at risk of developing diabetes. Image processing algorithms extract informative features from retinal images, capturing subtle abnormalities associated with early stages of diabetes. This system explores the application of machine learning, specifically Convolutional Neural Network (CNN) and Residual Networks (ResNet), in predicting ocular prognosis.The accuracy came upto the range of 98%. This system delves into how CNN and ResNet models effectively recognize patterns and features within images, contributing to a more nuanced understanding of ocular health. Eye Lens offers a non-invasive and cost-effective screening method, potentially enhancing early diabetic prediction in resource-constrained settings. The results demonstrate the potential of Eye Lens as an effective and accessible tool for early diabetic prediction, offering a valuable contribution to the field of preventive healthcare.

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