Autor segons l'article: Saleh, Emran; Blaszczynski, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riya-Fernandez, Sofia; Slowinsk, Roman;
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: ALI, EMRAN SALEH ALI / Moreno Ribas, Antonio / Romero Aroca, Pedro / Valls Mateu, Aïda
Paraules clau: 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
Resum: 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.
Àrees temàtiques: Saúde coletiva Medicine (miscellaneous) Medicina ii Medicina i Medical informatics Interdisciplinar Engineering, biomedical Engenharias iv Computer science, artificial intelligence Ciência da computação Artificial intelligence
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: antonio.moreno@urv.cat pedro.romero@urv.cat aida.valls@urv.cat
Identificador de l'autor: 0000-0003-3945-2314 0000-0002-7061-8987 0000-0003-3616-7809
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S0933365717300593
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Artificial Intelligence In Medicine. 85 50-63
Referència 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 de l'article: 10.1016/j.artmed.2017.09.006
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2018
Tipus de publicació: Journal Publications
Repositori URV