A Comparative Analysis of Risk Prediction Models for Diabetic Retinopathy using Machine Learning and Deep Learning
Keywords:
Deep Learning, Diabetic Retinopathy, Feature Extraction, Machine Learning, Computer Vision.Abstract
Diabetic Retinopathy (DR) is one of the leading causes of vision impairment among diabetic patients. With advancements in Machine Learning (ML) and Deep Learning (DL) techniques, predictive models for DR have significantly improved in accuracy and precision. This comparative analysis systematically explores various ML and DL approaches used in DR risk prediction, focusing on key techniques such as Convolutional Neural Networks (CNN), hybrid models, and advanced pre-processing methods. Following a comprehensive literature search from 2019 to 2024, using databases like Web of Science, Scopus, ResearchGate, ScienceDirect, and Springer, this review adheres to PRISMA guidelines to ensure methodological rigor. Studies have demonstrated promising results, with several models achieving high accuracy rates, such as 99.18% for vision-threatening DR detection. The key observation of this study is that deep learning, particularly with the latest technologies, outperforms traditional ML methods in every aspect of the prediction and classification of image datasets. However, challenges persist, particularly in terms of model generalization, data labeling, and computational complexity. This study provides a detailed comparative analysis of these techniques and identifies research gaps, including the integration of unsupervised learning methods and improving computational efficiency for real-world applications.
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