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Title: Evolutionary-based method for risk stratification of diabetic patients

Authors: Viorica Rozina Chifu; Emil Stefan Chifu; Cristina Bianca Pop; Ioan Salomie; Madalina Lupu

Addresses: Department of Computer Science, Technical University of Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Romania

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.

Keywords: patient risk stratification; evolutionary algorithms; clustering indexes.

DOI: 10.1504/IJIEI.2019.097549

International Journal of Intelligent Engineering Informatics, 2019 Vol.7 No.1, pp.37 - 60

Received: 25 Sep 2017
Accepted: 21 Feb 2018

Published online: 23 Jan 2019 *

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