Author, as appears in the article.: Romero-Aroca P; Valls A; Moreno A; Sagarra-Alamo R; Basora-Gallisa J; Saleh E; Baget-Bernaldiz M; Puig D

Department: Enginyeria Informàtica i Matemàtiques

URV's Author/s: ALI, EMRAN SALEH ALI / Basora Gallisa, Josep / Moreno Ribas, Antonio / Puig Valls, Domènec Savi / Romero Aroca, Pedro / Valls Mateu, Aïda

Keywords: Type-1 Retinopatía diabética Random forest test Prevalence Model Mellitus Medical decision making Fuzzy rules Follow-up Epidemiology E-health Diabetic retinopathy Diabetic macular edema Decision support systems Cost Clinical decision support system

Abstract: © 2019 Romero-Aroca et al. Background: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients. Method: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR). Results: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria. Discussion: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS. Conclusions: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

Thematic Areas: Saúde coletiva Odontología Medicine (miscellaneous) Medicina iii Medicina ii Medicina i Interdisciplinar Health information management Health informatics Health care sciences & services General medicine Engenharias iv Engenharias ii Enfermagem Educação física Ciência da computação Biotecnología Astronomia / física Arquitetura, urbanismo e design

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 15305627

Author's mail: josep.basora@urv.cat aida.valls@urv.cat pedro.romero@urv.cat josep.basora@urv.cat domenec.puig@urv.cat antonio.moreno@urv.cat

Author identifier: 0000-0003-3616-7809 0000-0002-7061-8987 0000-0002-0562-4205 0000-0003-3945-2314

Record's date: 2023-02-18

Journal volume: 25

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://www.liebertpub.com/doi/10.1089/tmj.2017.0282

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Telemedicine And E-Health. 25 (1): 31-40

APA: Romero-Aroca P; Valls A; Moreno A; Sagarra-Alamo R; Basora-Gallisa J; Saleh E; Baget-Bernaldiz M; Puig D (2019). A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application. Telemedicine And E-Health, 25(1), 31-40. DOI: 10.1089/tmj.2017.0282

Article's DOI: 10.1089/tmj.2017.0282

Entity: Universitat Rovira i Virgili

Journal publication year: 2019

Publication Type: Journal Publications