Temporal Dietary Patterns Using Kernel k-Means Clustering

Nitin Khanna; Heather A Eicher-Miller; Carol J Boushey; Saul B Gelfand; Edward J Delp

doi:10.1109/ISM.2011.68

Temporal Dietary Patterns Using Kernel k-Means Clustering

ISM. 2011:2011:375-380. doi: 10.1109/ISM.2011.68.

Authors

Nitin Khanna¹, Heather A Eicher-Miller², Carol J Boushey³, Saul B Gelfand, Edward J Delp

Affiliations

¹ School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA.
² Department of Nutrition Science, Purdue University, West Lafayette, Indiana, USA.
³ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.

Abstract

Chronic diseases, such as heart disease, diabetes, and obesity, have been linked with diet. Nutrient intake is also associated with diet. However, much of the research completed to elucidate these associations has not incorporated the concept of time. This paper introduces the concept of temporal dietary patterns and demonstrates a novel construct of 24-hour temporal dietary patterns for energy intake, present in a sample of the adult U.S. population 20 years and older (NHANES 1999-2004 dataset). An appropriate distance metric is proposed for comparing 24-hour diet records and is used with kernel k-means clustering to identify the temporal dietary patterns.