Evolutionary-based method for risk stratification of diabetic patients

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.

Systematic review of various feature extraction techniques for facial emotion recognition system

“…The Distinctiveness of human footprints features(Nagwanshi and Dubey, 2020) was estimated using G-mean clusters, segmentation, and rule based techniques. Authors(Chifu et al, 2019) proposed evolutionary based approach. Fast Fourier Transformation and principal component analysis techniques(Arabaci and Mohamed, 2020) were used for feature extraction.…”