International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In this project, we developed a hybrid modeling technique models K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM) models to estimate the severity of diabetic retinopathy using the APTOS dataset. By merging the strengths of these three algorithms, the model delivers more stable, accurate, and dependable predictions compared to individual classifiers. Such precise severity grading can support healthcare professionals by providing early warnings and timely insights, helping them plan proactive treatment for patients at risk. Beyond improving accuracy, the proposed ensemble method also reduces inconsistencies between different diagnostic systems, making it easier to integrate into existing medical workflows. This enhances overall diagnostic reliability and promotes better clinical decision-making. The study plays a vital role in applying machine learning in real-world healthcare settings, ultimately aiming to support clinicians and result that enhanced outcomes for patients with diabetic retinopathy. Diabetic retinopathy doesn't manifest as a singular entity but progresses through stages of severity. These stages, which include mild non-proliferative, moderate non-proliferative, severe non-proliferative, and proliferative DR.

Keywords: KNN, SVM model, Diabetic Retinopathy, APTOS, Healthcare professionals, Leveraging machine.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412122