Repositori institucional URV
| URV's Author/s: | ALI, EMRAN SALEH ALI / Moreno Ribas, Antonio / Romero Aroca, Pedro / Valls Mateu, Aïda |
| Author, as appears in the article.: | Saleh, Emran; Blaszczynski, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riya-Fernandez, Sofia; Slowinsk, Roman; |
| Author's mail: | antonio.moreno@urv.cat pedro.romero@urv.cat aida.valls@urv.cat |
| Author identifier: | 0000-0003-3945-2314 0000-0002-7061-8987 0000-0003-3616-7809 |
| Journal publication year: | 2018 |
| Publication Type: | Journal Publications |
| APA: | Saleh, Emran; Blaszczynski, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riya-Fernandez, Sofia; Slowinsk, Roman; (2018). Learning ensemble classifiers for diabetic retinopathy assessment. Artificial Intelligence In Medicine, 85(), 50-63. DOI: 10.1016/j.artmed.2017.09.006 |
| Papper original source: | Artificial Intelligence In Medicine. 85 50-63 |
| Abstract: | Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice. |
| Article's DOI: | 10.1016/j.artmed.2017.09.006 |
| Link to the original source: | https://www.sciencedirect.com/science/article/pii/S0933365717300593 |
| Papper version: | info:eu-repo/semantics/acceptedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Saúde coletiva Medicine (miscellaneous) Medicina ii Medicina i Medical informatics Interdisciplinar Engineering, biomedical Engenharias iv Computer science, artificial intelligence Ciência da computação Artificial intelligence |
| Keywords: | Uncertainty Rule-based models Random forest Medical informatics Fuzzy decision trees Ensemble classifiers Dominance-based rough set approach Diabetic retinopathy Decision support systems Decision making Data mining Class imbalance random forest fuzzy decision trees ensemble classifiers dominance-based rough set approach diabetic retinopathy decision support systems class imbalance |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-09-07 |
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