Understanding dietary patterns through stability-based validation using cluster and network analysis
Date of Publication
2025
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Statistics Major in Actuarial Science
Subject Categories
Mathematics
College
College of Science
Department/Unit
Mathematics and Statistics Department
Thesis Advisor
Angelyn R. Lao
Rechel G. Arcilla
Defense Panel Chair
Francis Joseph H. Campeña
Defense Panel Member
Shera Marie P. Boon
Abstract (English)
Understanding dietary patterns is crucial in explaining the widespread problem of triple-burden malnutrition. Common approaches include cluster and network analysis of food intake to identify dietary patterns by grouping food consumption. The framework developed in this study clustered food consumption determined through DNA trnL metabarcoding of plant taxa from collected 1,001 stool samples. Both approaches are utilized as a clustering solution to determine the most stable cluster through stability-based validation. Results indicate that the Leiden algorithm formed the most stable cluster with 3 group diets having different dietary patterns. Profiling done showed that participants of all group diets are at risk for obesity and are vulnerable to micronutrient deficiencies, while undernutrition was not evident in any of the groups. These findings can guide future research and the community to improve diets, lifestyles, and overall health, as well as lessen the impact of triple-burden malnutrition.
Abstract Format
html
Abstract (Filipino)
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Abstract Format
html
Language
English
Format
Electronic
Keywords
Malnutrition; System analysis; Cluster analysis
Recommended Citation
Ortega, A. R., & Guidote, A. P. (2025). Understanding dietary patterns through stability-based validation using cluster and network analysis. Retrieved from https://animorepository.dlsu.edu.ph/etdb_math/61
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Embargo Period
8-14-2028
