ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2019
K-means clustering of overweight and obese population using quantile-transformed metabolic data.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 15 citations in OpenAlex.
- Evaluation methodology for deep learning imputation models.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- Diabetic family history in young Japanese persons with normal glucose tolerance associates with k-means clustering of glucose response to oral glucose load, insulinogenic index and Matsuda index.Metabolism open · 2022Article
- Associations of specific-age and decade recall body mass index trajectories with obesity-related cancer.BMC cancer · 2021Article
- Categorization of β-cell capacity in patients with obesity via OGTT using K-means clustering.Endocrine connections · 2020Article
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3 authors at 2 institutions in 1 country.
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Abstract
objectiveUse of K-means clustering for big data technology to cluster an overweight and obese population metabolically.
methodsK-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.
resultsOverall, 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.
conclusionsThis 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.
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