Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Swamy, C. Manjunatha; Kumar, S. Babu; Yogish, Deepa; Kiran, B. Title A name given to the resource Automated Diabetic Retinopathy Diagnosis Using Ensemble Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1863 LNNS;pp.408-418 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-19182-3_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-19182-3_31</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039137901?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039137901?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Swamy C.M., Department of AI, ML and Data Science, School of Engineering and Technology, Christ University, Bengaluru, India; Kumar S.B., Department of AI, ML and Data Science, School of Engineering and Technology, Christ University, Bengaluru, India; Yogish D., Department of CSE, School of Engineering and Technology, Christ University, Bengaluru, India; Kiran B., Department of CSE, PES University, Bengaluru, India Description An account of the resource Diabetic Retinopathy is a major reason of vision impairment among diabetic patients, early and accurate diagnosis is crucial. This research focuses on developing a machine learning-based classification system to detect different stages of DR using Support Vector Machine (SVM), Random Forest (RF) and ensemble model. The dataset is divided into five categories: Healthy, Mild, Moderate, Proliferative and Severe DR. Performance evaluation using various metrics, including Accuracy, F1-score, RMSE and AUC-ROC, indicates that the ensemble model achieves the best results, with an accuracy of 77.66% and an AUC-ROC of 0.9015. The confusion matrices show that existing models struggle with certain misclassifications, the ensemble approach enhances overall predictive capability. Future improvements can include integrating deep learning models such as convolutional Neural Networks leveraging larger and more diverse datasets and incorporating image preprocessing techniques to enhance feature extraction. This system can help ophthalmologists to detect early and treatment planning, ultimately decrease the risk of blindness in diabetic patients. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Convolutional Neural Networks; Diabetic Retinopathy; Ensemble Model; Machine Learning; Random Forest; Support Vector Machine Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303219181-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online