论文标题
使用机器学习技术分析国家健康和营养状况调查数据中的肥胖子组：Brunei Darussalam的案例研究
Profiling Obese Subgroups in National Health and Nutritional Status Survey Data using Machine Learning Techniques: A Case Study from Brunei Darussalam
论文作者
论文摘要
Negara Brunei Darussalam卫生部每年进行国家健康和营养状况调查（NHANSS），以评估人口健康和营养模式和特征。这项研究的主要目的是通过应用数据减少和解释技术从NHANSS数据的肥胖样本中发现有意义的模式（组）。数据集中变量（定性和定量）的混合性质增加了研究的新颖性。因此，选择了分类主成分（CATPCA）技术来解释有意义的结果。肥胖与生活方式因素之间的关系，例如人口统计学，社会经济状况，体育锻炼，饮食行为，血压，糖尿病病史，糖尿病等。借助拆分方法技术对结果进行了验证，以对抗验证生成组的真实性。根据分析和结果，在数据集中发现了两个亚组，并且已经报道了这些亚组的显着特征。可以提出这些结果，以改善医疗保健行业。
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.
