K-means clustering of overweight and obese population using quantile-transformed metabolic data

Li Li,1 Qifa Song,2 Xi Yang11Department of Endocrinology and Metabolism, Ningbo First Hospital, Ningbo, People’s Republic of China; 2Department of Microbiology, Ningbo Municipal Centre for Disease Control and Prevention, Ningbo, People’s Republic of ChinaCorrespondence: Qifa Song...

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Autores principales:	Li L, Song Q, Yang X
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2019
Materias:	Overweight and obesity big data technology quantile-transformation K-means clustering Specialties of internal medicine RC581-951
Acceso en línea:	https://doaj.org/article/ab2aef1559a54647b6c2548e1cff73d3
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spelling	oai:doaj.org-article:ab2aef1559a54647b6c2548e1cff73d32021-12-02T10:37:43ZK-means clustering of overweight and obese population using quantile-transformed metabolic data1178-7007https://doaj.org/article/ab2aef1559a54647b6c2548e1cff73d32019-08-01T00:00:00Zhttps://www.dovepress.com/k-means-clustering-of-overweight-and-obese-population-using-quantile-t-peer-reviewed-article-DMSOhttps://doaj.org/toc/1178-7007Li Li,1 Qifa Song,2 Xi Yang11Department of Endocrinology and Metabolism, Ningbo First Hospital, Ningbo, People’s Republic of China; 2Department of Microbiology, Ningbo Municipal Centre for Disease Control and Prevention, Ningbo, People’s Republic of ChinaCorrespondence: Qifa SongDepartment of Microbiology, Ningbo Municipal Centre for Disease Control and Prevention, No. 237, Yongfeng Road, Ningbo, Zhejiang Province 315010, People’s Republic of ChinaTel +86 05 748 727 4563Email qifasong@126.comObjective: Use of K-means clustering for big data technology to cluster an overweight and obese population metabolically.Methods: K-means clustering with the help of quantile transformation of attribute values was applied to overcome the impact of the considerable variation in the values of obesity attributes involving outliers and skewed distribution.Results: Overall, 447 subjects were categorized into six clusters; metabolically normal, mild, and severe categories. There were clearly separated metabolically normal Cluster 1 and severe Cluster 2, as well as intermediate Cluster 3, 4, and 5 that had profiles of fewer attributes with abnormal values. Cluster 3 was characteristic of sole hypertension. Cluster 3 and 4 exhibited contrasting HDL-C and LDL-C levels despite similarly elevated total cholesterol. Cluster 6 with slightly elevated triglyceride was closest to the normal group. Four- and 10-quantile-transformations yielded consistent clustering results. Compared with the original data, the quantile-transformed data produced more regular and spherical clusters and evenly distributed clusters in terms of object numbers.Conclusions: This big data analysis strategy makes use of quantile-transformation of data to overcome the issue of outliers and the irregular distribution and applies to the analysis of other non-communicable diseases.Keywords: overweight and obesity, big data technology, quantile-transformation, K-means clusteringLi LSong QYang XDove Medical PressarticleOverweight and obesitybig data technologyquantile-transformationK-means clusteringSpecialties of internal medicineRC581-951ENDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Vol Volume 12, Pp 1573-1582 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Overweight and obesity big data technology quantile-transformation K-means clustering Specialties of internal medicine RC581-951
spellingShingle	Overweight and obesity big data technology quantile-transformation K-means clustering Specialties of internal medicine RC581-951 Li L Song Q Yang X K-means clustering of overweight and obese population using quantile-transformed metabolic data
description	Li Li,1 Qifa Song,2 Xi Yang11Department of Endocrinology and Metabolism, Ningbo First Hospital, Ningbo, People’s Republic of China; 2Department of Microbiology, Ningbo Municipal Centre for Disease Control and Prevention, Ningbo, People’s Republic of ChinaCorrespondence: Qifa SongDepartment of Microbiology, Ningbo Municipal Centre for Disease Control and Prevention, No. 237, Yongfeng Road, Ningbo, Zhejiang Province 315010, People’s Republic of ChinaTel +86 05 748 727 4563Email qifasong@126.comObjective: Use of K-means clustering for big data technology to cluster an overweight and obese population metabolically.Methods: K-means clustering with the help of quantile transformation of attribute values was applied to overcome the impact of the considerable variation in the values of obesity attributes involving outliers and skewed distribution.Results: Overall, 447 subjects were categorized into six clusters; metabolically normal, mild, and severe categories. There were clearly separated metabolically normal Cluster 1 and severe Cluster 2, as well as intermediate Cluster 3, 4, and 5 that had profiles of fewer attributes with abnormal values. Cluster 3 was characteristic of sole hypertension. Cluster 3 and 4 exhibited contrasting HDL-C and LDL-C levels despite similarly elevated total cholesterol. Cluster 6 with slightly elevated triglyceride was closest to the normal group. Four- and 10-quantile-transformations yielded consistent clustering results. Compared with the original data, the quantile-transformed data produced more regular and spherical clusters and evenly distributed clusters in terms of object numbers.Conclusions: This big data analysis strategy makes use of quantile-transformation of data to overcome the issue of outliers and the irregular distribution and applies to the analysis of other non-communicable diseases.Keywords: overweight and obesity, big data technology, quantile-transformation, K-means clustering
format	article
author	Li L Song Q Yang X
author_facet	Li L Song Q Yang X
author_sort	Li L
title	K-means clustering of overweight and obese population using quantile-transformed metabolic data
title_short	K-means clustering of overweight and obese population using quantile-transformed metabolic data
title_full	K-means clustering of overweight and obese population using quantile-transformed metabolic data
title_fullStr	K-means clustering of overweight and obese population using quantile-transformed metabolic data
title_full_unstemmed	K-means clustering of overweight and obese population using quantile-transformed metabolic data
title_sort	k-means clustering of overweight and obese population using quantile-transformed metabolic data
publisher	Dove Medical Press
publishDate	2019
url	https://doaj.org/article/ab2aef1559a54647b6c2548e1cff73d3
work_keys_str_mv	AT lil kmeansclusteringofoverweightandobesepopulationusingquantiletransformedmetabolicdata AT songq kmeansclusteringofoverweightandobesepopulationusingquantiletransformedmetabolicdata AT yangx kmeansclusteringofoverweightandobesepopulationusingquantiletransformedmetabolicdata
_version_	1718396957562503168

K-means clustering of overweight and obese population using quantile-transformed metabolic data

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