Profiling Obese Subgroups in National Health and Nutritional Status Survey Data using Machine Learning Techniques: A Case Study from Brunei Darussalam

TL;DR

This study uses machine learning, specifically CATPCA, to identify meaningful obese subgroups in Brunei's health survey data, revealing key lifestyle and demographic patterns for improved healthcare strategies.

Contribution

It introduces a novel application of CATPCA for subgroup profiling in health data with mixed variable types, validated through split method verification.

Findings

Two distinct obese subgroups identified

Key lifestyle and demographic factors associated with each subgroup

Results support targeted healthcare interventions

Abstract

National Health and Nutritional Status Survey (NHANSS) is conducted annually by the Ministry of Health in Negara Brunei Darussalam to assess the population health and nutritional patterns and characteristics. The main aim of this study was to discover meaningful patterns (groups) from the obese sample of NHANSS data by applying data reduction and interpretation techniques. The mixed nature of the variables (qualitative and quantitative) in the data set added novelty to the study. Accordingly, the Categorical Principal Component (CATPCA) technique was chosen to interpret the meaningful results. The relationships between obesity and the lifestyle factors like demography, socioeconomic status, physical activity, dietary behavior, history of blood pressure, diabetes, etc., were determined based on the principal components generated by CATPCA. The results were validated with the help of the split method technique to counter verify the authenticity of the generated groups. Based on the analysis and results, two subgroups were found in the data set, and the salient features of these subgroups have been reported. These results can be proposed for the betterment of the healthcare industry.