Machine Learning for Predictive Healthcare: Early Diagnosis of Diabetic Retinopathy in India

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness in India, and early detection can significantly reduce its impact. This paper proposes a machine learning-based predictive model for the early diagnosis of DR using retinal imaging. The study evaluates various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNN), to develop a reliable classification model. Using a dataset from leading Indian hospitals, the paper demonstrates the effectiveness of these models in detecting DR at its early stages. The challenges in data collection, such as the need for high-quality retinal images and the lack of accessible diagnostic tools in rural areas, are also discussed.