Editorial
Diabetic Retinopathy Eye Disease Detection Using Machine Learning
@ARTICLE{10.4108/eetiot.5349, author={Ruby Dahiya and Nidhi Agarwal and Sangeeta Singh and Deepanshu Verma and Shivam Gupta}, title={Diabetic Retinopathy Eye Disease Detection Using Machine Learning}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={3}, keywords={Eye Disease Detection, Machine Learning, K-Nearest Neighbours, Support Vector Machine, Convolutional Neural Network}, doi={10.4108/eetiot.5349} }- Ruby Dahiya
Nidhi Agarwal
Sangeeta Singh
Deepanshu Verma
Shivam Gupta
Year: 2024
Diabetic Retinopathy Eye Disease Detection Using Machine Learning
IOT
EAI
DOI: 10.4108/eetiot.5349
Abstract
INTRODUCTION: Diabetic retinopathy is the name given to diabetes problems that harm the eyes. Its root cause is damage to the blood capillaries in the tissue that is light-sensitive in the rear of the eye. Over time, having excessive blood sugar may cause to the tiny blood capillaries that nourish the retina to become blocked, severing the retina's blood circulation. As a result, the eye tries to develop new blood arteries. OBJECTIVES: The objective of this research is to analyse and compare various algorithms based on their performance and efficiency in predicting Diabetic Retinopathy. METHODS: To achieve this, an experimental model was developed to predict Diabetic Retinopathy at early stage. RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of various algorithm selection and its impact on the overall performance of models. CONCLUSION: The findings of this study contribute to the understanding of multiple algorithm selection and its impact on the overall performance of models’ accuracy. By applying these algorithms, we can predict disease at early stage such that it can be cured efficiently before it goes worse.
Copyright © 2024 R. Dahiya et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
