sis 18: e11

Research Article

Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods

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  • @ARTICLE{10.4108/eai.13-7-2018.165505,
        author={Shiva Shankar Reddy and Nilambar Sethi and R. Rajender},
        title={Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={7},
        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},
        doi={10.4108/eai.13-7-2018.165505}
    }
    
  • Shiva Shankar Reddy
    Nilambar Sethi
    R. Rajender
    Year: 2020
    Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.165505
Shiva Shankar Reddy1,*, Nilambar Sethi2, R. Rajender3
  • 1: Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, India
  • 2: Department of Computer Science and Engineering, GIET, Gunupur, Odisha, India
  • 3: Department of Computer Science and Engineering, LENDI Engineering College, Vizianagaram, India
*Contact email: shiva.shankar591@gmail.com

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.