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National Health and Nutritional Status Survey (NHANSS) is conducted annuallyby the Ministry of Health in Negara Brunei Darussalam to assess the populationhealth and nutritional patterns and characteristics. The main aim of this studywas to discover meaningful patterns (groups) from the obese sample of NHANSSdata by applying data reduction and interpretation techniques. The mixed natureof the variables (qualitative and quantitative) in the data set added noveltyto the study. Accordingly, the Categorical Principal Component (CATPCA)technique was chosen to interpret the meaningful results. The relationshipsbetween obesity and the lifestyle factors like demography, socioeconomicstatus, physical activity, dietary behavior, history of blood pressure,diabetes, etc., were determined based on the principal components generated byCATPCA. The results were validated with the help of the split method techniqueto counter verify the authenticity of the generated groups. Based on theanalysis and results, two subgroups were found in the data set, and the salientfeatures of these subgroups have been reported. These results can be proposedfor the betterment of the healthcare industry.

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