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Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data

(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy base...

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Autores principales: Shen, Zun, Wu, Qingfeng, Wang, Zhi, Chen, Guoyi, Lin, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197325/
https://www.ncbi.nlm.nih.gov/pubmed/34070287
http://dx.doi.org/10.3390/s21113663
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author Shen, Zun
Wu, Qingfeng
Wang, Zhi
Chen, Guoyi
Lin, Bin
author_facet Shen, Zun
Wu, Qingfeng
Wang, Zhi
Chen, Guoyi
Lin, Bin
author_sort Shen, Zun
collection PubMed
description (1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis.
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spelling pubmed-81973252021-06-13 Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data Shen, Zun Wu, Qingfeng Wang, Zhi Chen, Guoyi Lin, Bin Sensors (Basel) Article (1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis. MDPI 2021-05-25 /pmc/articles/PMC8197325/ /pubmed/34070287 http://dx.doi.org/10.3390/s21113663 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shen, Zun
Wu, Qingfeng
Wang, Zhi
Chen, Guoyi
Lin, Bin
Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
title Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
title_full Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
title_fullStr Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
title_full_unstemmed Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
title_short Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data
title_sort diabetic retinopathy prediction by ensemble learning based on biochemical and physical data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197325/
https://www.ncbi.nlm.nih.gov/pubmed/34070287
http://dx.doi.org/10.3390/s21113663
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