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 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
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 Health informatics 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: 2024-10-12
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