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

Abstract

Objective: 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.