Autor según el artículo: Saleh, Emran; Blaszczynski, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riya-Fernandez, Sofia; Slowinsk, Roman
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: ALI, EMRAN SALEH ALI / Moreno Ribas, Antonio / Romero Aroca, Pedro / Valls Mateu, Aïda
Palabras clave: 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
Resumen: 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.
Áreas temáticas: 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
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: antonio.moreno@urv.cat pedro.romero@urv.cat aida.valls@urv.cat
Identificador del autor: 0000-0003-3945-2314 0000-0002-7061-8987 0000-0003-3616-7809
Fecha de alta del registro: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0933365717300593
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Artificial Intelligence In Medicine. 85 50-63
Referencia de l'ítem segons les normes 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
DOI del artículo: 10.1016/j.artmed.2017.09.006
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2018
Tipo de publicación: Journal Publications
Repositori URV