Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Ganjar Alfian, Muhammad Syafrudin, Norma Latif Fitriyani, Muhammad Anshari, Pavel Stasa, Jiri Svub and Jongtae Rhee
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Ganjar Alfian: Industrial Artificial Intelligence (AI) Research Center, Nano Information Technology Academy, Dongguk University, Seoul 04626, Korea
Muhammad Syafrudin: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Norma Latif Fitriyani: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Muhammad Anshari: School of Business & Economics, Universiti Brunei Darussalam, Gadong BE1410, Brunei
Pavel Stasa: Department of Economics and Control Systems, Faculty of Mining and Geology, VSB–Technical University of Ostrava, 70800 Ostrava-Poruba, Czech Republic
Jiri Svub: Department of Economics and Control Systems, Faculty of Mining and Geology, VSB–Technical University of Ostrava, 70800 Ostrava-Poruba, Czech Republic
Jongtae Rhee: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea

Mathematics, 2020, vol. 8, issue 9, 1-19

Abstract: 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.

Keywords: retinopathy; risk factor; machine learning; deep neural network; recursive feature elimination; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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