Advancement in Diabetic Retinopathy Prediction: Utilizing Voting Classifiers Techniques for Early Detection
Received: 4 September 2025 | Revised: 14 November 2025 and 25 November 2025 | Accepted: 26 November 2025 | Online: 9 February 2026
Corresponding author: P. Kavitha
Abstract
Diabetic Retinopathy (DR), also known as diabetic eye disease, damages the retina and is linked to diabetes mellitus. According to previous studies, DR influences up to 80% of individuals and children who have had type I and II diabetes for more than 20 years. However, with proper care and cautious eye monitoring, severe blind forms of retinopathy and maculopathy can be avoided. This study used a voting classifier, combining the predictions of base models, such as Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosting (GB) classifiers, to improve the performance and accuracy of predictions.
Keywords:
deep learning, ensemble techniques, voting classifier, random forest, Support Vector Machine (SVM), Gradient Boosting (GB)Downloads
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Copyright (c) 2025 Choon Kit Chan, P. Kavitha, A. Kalaivani, Goutami Chenumalla, A. Vanathi, K. H. Koushika, G. Likhitha
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