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dc.creatorFerreira, Ana Cláudia Barbosa Honório-
dc.creatorFerreira, Danton Diego-
dc.creatorOliveira, Henrique Ceretta-
dc.creatorResende, Igor Carvalho de-
dc.creatorAnjos, André-
dc.creatorLopes, Maria Helena Baena de Moraes-
dc.date.accessioned2020-08-14T18:39:26Z-
dc.date.available2020-08-14T18:39:26Z-
dc.date.issued2020-05-
dc.identifier.citationFERREIRA, A. C. B. H. et al. Competitive neural layer-based method to identify people with high risk for diabetic foot. Computers in Biology and Medicine, [S. I.], v. 120, May 2020. DOI: https://doi.org/10.1016/j.compbiomed.2020.103744.pt_BR
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2020.103744pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/42427-
dc.description.abstractBackground and objective: To automatically identify patients with diabetes mellitus (DM) who have high risk of developing diabetic foot, via an unsupervised machine learning technique. Methods: We collected a new database containing 54 known risk factors from 250 patients diagnosed with diabetes mellitus. The database also contained a separate validation cohort composed of 73 subjects, where the perceived risk was annotated by expert nurses. A competitive neuron layer-based method was used to automatically split training data into two risk groups. Results: We found that one of the groups was composed of patients with higher risk of developing diabetic foot. The dominant variables that described group membership via our method agreed with the findings from other studies, and indicated a greater risk for developing such a condition. Our method was validated on the available test data, reaching 71% sensitivity, 100% specificity, and 90% accuracy. Conclusions: Unsupervised learning may be deployed to screen patients with diabetes mellitus, pointing out high-risk individuals who require priority follow-up in the prevention of diabetic foot with very high accuracy. The proposed method is automatic and does not require clinical examinations to perform risk assessment, being solely based on the information of a questionnaire answered by patients. Our study found that discriminant variables for predicting risk group membership are highly correlated with expert opinion.pt_BR
dc.languageenpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceComputers in Biology and Medicinept_BR
dc.subjectDiabetes Mellituspt_BR
dc.subjectDiabetic footpt_BR
dc.subjectArtificial neural networkpt_BR
dc.subjectPé diabéticopt_BR
dc.subjectRede neural artificialpt_BR
dc.titleCompetitive neural layer-based method to identify people with high risk for diabetic footpt_BR
dc.typeArtigopt_BR
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