2021
Abreu, A.; Ferreira, D.; Neto, C.; Abelha, A.; Machado, J.
Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques Proceedings Article
Em: T., Antipova (Ed.): pp. 198-209, Springer Science and Business Media Deutschland GmbH, 2021, ISSN: 21945357, (cited By 2; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499).
Resumo | Links | BibTeX | Etiquetas: Classification models; Data mining models; Data mining process; Diabetic retinopathy; Eye fundus; Logistic regression algorithms; Mining classification; Sampling method, Computer aided diagnosis; Eye protection; Logistic regression; Medical imaging, Data mining
@inproceedings{Abreu2021198,
title = {Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques},
author = {A. Abreu and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Antipova T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103501921&doi=10.1007%2f978-3-030-71782-7_18&partnerID=40&md5=00cbc654502f69e638aa251a4aad2b0f},
doi = {10.1007/978-3-030-71782-7_18},
issn = {21945357},
year = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1352},
pages = {198-209},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Diabetic retinopathy is one of the complications of diabetes that affects the small vessels of the retina, being the main cause of blindness in adults. An early detection of this disease is essential, as it can prevent blindness as well as other irreversible harmful outcomes. This article attempts to develop a data mining model capable of identifying diabetic retinopathy in patients based on features extracted from eye fundus images. The data mining process was carried out in the RapidMiner software and followed the CRISP-DM methodology. In particular, classification models were built by combining different scenarios, algorithms, and sampling methods. The data mining model which performed best achieved an accuracy of 76.90%, a precision of 85.92%, and a sensitivity of 67.40%, using the Logistic Regression algorithm and Split Validation as the sampling method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},
keywords = {Classification models; Data mining models; Data mining process; Diabetic retinopathy; Eye fundus; Logistic regression algorithms; Mining classification; Sampling method, Computer aided diagnosis; Eye protection; Logistic regression; Medical imaging, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Diabetic retinopathy is one of the complications of diabetes that affects the small vessels of the retina, being the main cause of blindness in adults. An early detection of this disease is essential, as it can prevent blindness as well as other irreversible harmful outcomes. This article attempts to develop a data mining model capable of identifying diabetic retinopathy in patients based on features extracted from eye fundus images. The data mining process was carried out in the RapidMiner software and followed the CRISP-DM methodology. In particular, classification models were built by combining different scenarios, algorithms, and sampling methods. The data mining model which performed best achieved an accuracy of 76.90%, a precision of 85.92%, and a sensitivity of 67.40%, using the Logistic Regression algorithm and Split Validation as the sampling method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.