Evolutionary-based method for risk stratification of diabetic patients. (2019)

• Record Type: Journal Article
• Authors: Chifu, Viorica Rozina; Chifu, Emil Stefan; Pop, Cristina Bianca; Salomie, Ioan; Lupu, Madalina
• Is Part Of: International journal of intelligent engineering informatics. Volume 7:Number 1(2019)
• Page Start: 37
• Page End: 60
• Publication Date: 2019
• Subjects: patient risk stratification -- evolutionary algorithms -- clustering indexes; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

Biologically-inspired computing is an interdisciplinary research domain that brings together principles from mathematics, computer science and biology in order to develop intelligent algorithms or high performance computing models that are able to capture the social behaviour of animals, insects, birds or other living organisms. Recently, bio-inspired computing has been successfully applied for solving problems in the e-health domain. This chapter addresses the problem of optimality in the e-health domain by proposing an evolutionary-inspired method for clustering patients according to the risk of having diabetes. This method clusters patients based on their similarity with respect to the following features: age, sex, race category, body mass index, whether the patient has or has not hypertension, and the presence or absence of first-degree relatives with diabetes. Our method has been tested on the NHANESIII dataset.