Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F20%3A10245344" target="_blank" >RIV/61989100:27350/20:10245344 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2227-7390/8/9/1620" target="_blank" >https://www.mdpi.com/2227-7390/8/9/1620</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/math8091620" target="_blank" >10.3390/math8091620</a>

Result language
angličtina
Original language name
Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/LTE117005" target="_blank" >LTE117005: Auto-ID technology and the Internet of Things to enhance the quality of health services</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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