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Cluster analysis for identifying obesity subgroups in health and nutritional status survey data

This study presents the discovery of meaningful patterns (groups) from the obese samples of health and nutritional survey data by applying various clustering techniques. Due to the mixed nature of the data (qualitative and quantitative variables) in the data set, the best-suited clustering technique...

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Main Authors: Khalil, Usman, Ahmed Malik, Owais, Teck, Daphne Ching Lai, Ong, Sok King
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/17963/1/11.pdf
http://journalarticle.ukm.my/17963/
https://www.ukm.my/apjitm/articles-year.php
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spelling my-ukm.journal.179632022-01-15T08:20:42Z http://journalarticle.ukm.my/17963/ Cluster analysis for identifying obesity subgroups in health and nutritional status survey data Khalil, Usman Ahmed Malik, Owais Teck, Daphne Ching Lai Ong, Sok King This study presents the discovery of meaningful patterns (groups) from the obese samples of health and nutritional survey data by applying various clustering techniques. Due to the mixed nature of the data (qualitative and quantitative variables) in the data set, the best-suited clustering techniques with appropriate dissimilarity metrics were chosen to interpret the meaningful results. The relationships between obesity and the lifestyle affecting factors like demography, socio-economic status, physical activity, and dietary behavior were assessed using four cluster techniques namely Two-Step clustering, Partition Around Medoids (PAM), Agglomerative Hierarchical clustering and, Kohonen Self Organizing Maps (SOMs). The solutions generated by these techniques were analyzed and validated by the help of cluster validity (CV) indices and later on their associations were determined with the obesity classes to discover the pattern from the obese sample. Two-Step clustering and hierarchical clustering outperformed the other applied techniques in identifying the subgroups based on the underlying hidden patterns in the data. Based on the CV indices values and the association analysis (obesity factor with the cluster solutions), two subgroups were generated and profiles of these groups have been reported. The first group belonged to the middle-aged individuals who seem to take care of their lifestyle while the other group belonged to young-aged individuals who in contrast to the first group presented a careless lifestyle factor (i.e., physical activity and dietary behavior). The salient features of these subgroups have been reported and can be proposed for the betterment in the health care industry. The research helped in identifying the interesting subsets/groups within survey data demonstrating similar characteristics and health status (i.e., prevalence of obesity with respect to lifestyle factors like physical activity, dietary behavior etc.) which will help to suggest appropriate measures/steps to be taken by the concerned departments to counter them and prevent in the population. Penerbit Universiti Kebangsaan Malaysia 2021-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17963/1/11.pdf Khalil, Usman and Ahmed Malik, Owais and Teck, Daphne Ching Lai and Ong, Sok King (2021) Cluster analysis for identifying obesity subgroups in health and nutritional status survey data. Asia-Pacific Journal of Information Technology and Multimedia, 10 (2). pp. 146-169. ISSN 2289-2192 https://www.ukm.my/apjitm/articles-year.php
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description This study presents the discovery of meaningful patterns (groups) from the obese samples of health and nutritional survey data by applying various clustering techniques. Due to the mixed nature of the data (qualitative and quantitative variables) in the data set, the best-suited clustering techniques with appropriate dissimilarity metrics were chosen to interpret the meaningful results. The relationships between obesity and the lifestyle affecting factors like demography, socio-economic status, physical activity, and dietary behavior were assessed using four cluster techniques namely Two-Step clustering, Partition Around Medoids (PAM), Agglomerative Hierarchical clustering and, Kohonen Self Organizing Maps (SOMs). The solutions generated by these techniques were analyzed and validated by the help of cluster validity (CV) indices and later on their associations were determined with the obesity classes to discover the pattern from the obese sample. Two-Step clustering and hierarchical clustering outperformed the other applied techniques in identifying the subgroups based on the underlying hidden patterns in the data. Based on the CV indices values and the association analysis (obesity factor with the cluster solutions), two subgroups were generated and profiles of these groups have been reported. The first group belonged to the middle-aged individuals who seem to take care of their lifestyle while the other group belonged to young-aged individuals who in contrast to the first group presented a careless lifestyle factor (i.e., physical activity and dietary behavior). The salient features of these subgroups have been reported and can be proposed for the betterment in the health care industry. The research helped in identifying the interesting subsets/groups within survey data demonstrating similar characteristics and health status (i.e., prevalence of obesity with respect to lifestyle factors like physical activity, dietary behavior etc.) which will help to suggest appropriate measures/steps to be taken by the concerned departments to counter them and prevent in the population.
format Article
author Khalil, Usman
Ahmed Malik, Owais
Teck, Daphne Ching Lai
Ong, Sok King
spellingShingle Khalil, Usman
Ahmed Malik, Owais
Teck, Daphne Ching Lai
Ong, Sok King
Cluster analysis for identifying obesity subgroups in health and nutritional status survey data
author_facet Khalil, Usman
Ahmed Malik, Owais
Teck, Daphne Ching Lai
Ong, Sok King
author_sort Khalil, Usman
title Cluster analysis for identifying obesity subgroups in health and nutritional status survey data
title_short Cluster analysis for identifying obesity subgroups in health and nutritional status survey data
title_full Cluster analysis for identifying obesity subgroups in health and nutritional status survey data
title_fullStr Cluster analysis for identifying obesity subgroups in health and nutritional status survey data
title_full_unstemmed Cluster analysis for identifying obesity subgroups in health and nutritional status survey data
title_sort cluster analysis for identifying obesity subgroups in health and nutritional status survey data
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2021
url http://journalarticle.ukm.my/17963/1/11.pdf
http://journalarticle.ukm.my/17963/
https://www.ukm.my/apjitm/articles-year.php
_version_ 1724074632506507264
score 13.211869

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