Competitive neural layer-based method to identify people with high risk for diabetic foot. (May 2020)

• Record Type: Journal Article
• Authors: Ferreira, Ana Cláudia Barbosa Honório; Ferreira, Danton Diego; Oliveira, Henrique Ceretta; Resende, Igor Carvalho de; Anjos, André; Lopes, Maria Helena Baena de Moraes
• Is Part Of: Computers in biology and medicine. Volume 120(2020)
• Publication Date: 2020-05
• Subjects: Diabetes mellitus -- Diabetic foot -- Artificial neural network; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103744
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background 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. Highlights: A new system was developed to identify people with high risk to develop diabetic foot. The system exploits the generalization capacity of the competitive neural network. The system takes advantage of being non-invasive, i.e. without clinical exams. It is useful to triage people with DM by pointing out high-risk patients. A software system was also developed and it processes and stores the information.