Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors
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Industrial Artificial Intelligence (AI) Research Center, Nano Information Technology Academy, Dongguk University, Seoul 04626, Korea
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Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
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School of Business & Economics, Universiti Brunei Darussalam, Gadong BE1410, Brunei
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Department of Economics and Control Systems, Faculty of Mining and Geology, VSB–Technical University of Ostrava, 70800 Ostrava-Poruba, Czech Republic
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(9), 1620; https://doi.org/10.3390/math8091620
Received: 21 August 2020 / Revised: 11 September 2020 / Accepted: 17 September 2020 / Published: 19 September 2020
(This article belongs to the Special Issue Advances in Mathematical Methods for Machine Learning Algorithms for Computer Aided Diagnostic Systems)
Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.
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Keywords:
retinopathy; risk factor; machine learning; deep neural network; recursive feature elimination; deep learning
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MDPI and ACS Style
Alfian, G.; Syafrudin, M.; Fitriyani, N.L.; Anshari, M.; Stasa, P.; Svub, J.; Rhee, J. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics 2020, 8, 1620. https://doi.org/10.3390/math8091620
AMA Style
Alfian G, Syafrudin M, Fitriyani NL, Anshari M, Stasa P, Svub J, Rhee J. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics. 2020; 8(9):1620. https://doi.org/10.3390/math8091620
Chicago/Turabian StyleAlfian, Ganjar, Muhammad Syafrudin, Norma L. Fitriyani, Muhammad Anshari, Pavel Stasa, Jiri Svub, and Jongtae Rhee. 2020. "Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors" Mathematics 8, no. 9: 1620. https://doi.org/10.3390/math8091620
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