Author, as appears in the article.: Saleh, Emran; Blaszczynski, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riya-Fernandez, Sofia; Slowinsk, Roman
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: ALI, EMRAN SALEH ALI / Moreno Ribas, Antonio / Romero Aroca, Pedro / Valls Mateu, Aïda
Keywords: Uncertainty Time factors Rule-based models Risk factors Risk assessment Reproducibility of results Random forest Prognosis Predictive value of tests Medical informatics Machine learning Humans Fuzzy logic Fuzzy decision trees Ensemble classifiers Electronic health records Dominance-based rough set approach Diabetic retinopathy Diabetes mellitus, type 2 Diabetes mellitus, type 1 Decision trees Decision support techniques Decision support systems, clinical Decision support systems Decision making Data mining Clinical decision-making Class imbalance random forest fuzzy decision trees ensemble classifiers dominance-based rough set approach diabetic retinopathy decision support systems class imbalance
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.
Thematic Areas: Saúde coletiva Medicine (miscellaneous) Medicina ii Medicina i Medical informatics Interdisciplinar Health informatics Engineering, biomedical Engenharias iv Computer science, artificial intelligence Ciência da computação Artificial intelligence
licence for use: https://creativecommons.org/licenses/by/3.0/es/
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
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S0933365717300593
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Artificial Intelligence In Medicine. 85 50-63
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
Article's DOI: 10.1016/j.artmed.2017.09.006
Entity: Universitat Rovira i Virgili
Journal publication year: 2018
Publication Type: Journal Publications
Repositori URV