Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods

Reddy, Shiva Shankar and Sethi, Nilambar and Rajender, R. (2021) Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods. EAI Endorsed Transactions on Scalable Information Systems, 8 (29). e1. ISSN 2032-9407

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Abstract

Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that efficiently predicts diabetic retinopathy. Machine learning techniques like Decision tree, Random forest, Adaptive boosting and Bagging are used as primary algorithms to train predictive models. An algorithm namely ‘Support Vector Machine using Gaussian kernel for retinopathy prediction’ is proposed in this work. The proposed algorithm is compared with the primary algorithms based on five evaluation metrics namely accuracy, Youden’s J index, concordance, Somers’ D statistic and balanced accuracy. From the results obtained the proposed algorithm obtained better values for all considered evaluation metrics. Thus the use of SVM with Gaussian kernel is proposed to be used for prediction of diabetic retinopathy.

Item Type: Article
Uncontrolled Keywords: Diabetic retinopathy, random forest, decision tree, adaptive boosting, bagging, support vector machine (SVM) using Gaussian kernel (GK), accuracy, Youden’s J index, concordance, Somers’ D statistic and balanced accuracy
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 04 Feb 2021 14:27
Last Modified: 04 Feb 2021 14:27
URI: https://eprints.eudl.eu/id/eprint/965

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