research-article

Risk Quantification of Metabolic Syndrome with Quantum Particle Swarm Optimisation

Authors:

Habeebah A. Kakudi,

Kitsuchart PasupaAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 1141 - 1147

https://doi.org/10.1145/3041021.3054935

Published: 03 April 2017 Publication History

Abstract

Metabolic syndrome (MetS) is a combination of interrelated risk factors associated with an increased risk of developing type II diabetes Mellitus (T2DM), stroke and cardiovascular diseases (CVD). The economic, social and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Currently, the traditional method of MetS diagnosis is based on dichotomised definitions provided by various expert health organisations. However, this method is laced with the indetermination of MetS in individuals with borderline risk factor values due to a binary diagnosis and the assumption of equal weighting for all risk factors during diagnosis. The purpose of this paper is to examine the use of the MetS areal similarity degree risk analysis based on weighted radar charts comprising of diagnostic thresholds and risk factor results of an individual. We further enhance this risk quantification method by applying quantum particle swarm optimization to derive the weights. The proposed risk quantification was carried out using a sample of 528 individuals from an examination survey conducted between 2007 and 2014 in Serbia. The results are evaluated with the traditional dichotomised method of MetS diagnosis, in this case the joint interim statement (JIS). The results obtained showed that the proposed risk quantification method outperformed the dichotomised method at diagnosing MetS even in individuals who present risk factor examination values at the threshold borderlines.

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Cited By

Sghaireen MAl-Smadi YAl-Qerem ASrivastava KGanji KAlam MNashwan SKhader Y(2022)Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based ClassificationDiagnostics10.3390/diagnostics1212311712:12(3117)Online publication date: 10-Dec-2022
https://doi.org/10.3390/diagnostics12123117
Kakudi HLoo CMoy FMasuyama NPasupa K(2019)Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAPIEEE Access10.1109/ACCESS.2018.28802247(8437-8453)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2880224
Khashman ASerakinci NKizilkanat M(2019)Metabolic Syndrome Risk Evaluation Based on VDR Polymorphisms and Neural Networks10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-201910.1007/978-3-030-35249-3_126(943-949)Online publication date: 20-Nov-2019
https://doi.org/10.1007/978-3-030-35249-3_126

Index Terms

Risk Quantification of Metabolic Syndrome with Quantum Particle Swarm Optimisation
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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cover image ACM Other conferences

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Sghaireen MAl-Smadi YAl-Qerem ASrivastava KGanji KAlam MNashwan SKhader Y(2022)Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based ClassificationDiagnostics10.3390/diagnostics1212311712:12(3117)Online publication date: 10-Dec-2022
https://doi.org/10.3390/diagnostics12123117
Kakudi HLoo CMoy FMasuyama NPasupa K(2019)Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAPIEEE Access10.1109/ACCESS.2018.28802247(8437-8453)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2880224
Khashman ASerakinci NKizilkanat M(2019)Metabolic Syndrome Risk Evaluation Based on VDR Polymorphisms and Neural Networks10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-201910.1007/978-3-030-35249-3_126(943-949)Online publication date: 20-Nov-2019
https://doi.org/10.1007/978-3-030-35249-3_126

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