Cluster Analysis for Identifying Obesity Subrouops in Health and Nutritional Status Survey Data

Abstract

This study presents the discovery of meaningful patterns (groups) from the obese samples of health and nutritional survey data by applying various clustering techniques. Due to the mixed nature of the data (qualitative and quantitative variables) in the data set, the best-suited clustering techniques with appropriate dissimilarity metrics were chosen to interpret the meaningful results. The relationships between obesity and the lifestyle affecting factors like demography, socio-economic status, physical activity, and dietary behavior were assessed using four cluster techniques namely Two-Step clustering, Partition Around Medoids (PAM), Agglomerative Hierarchical clustering and, Kohonen Self Organizing Maps (SOMs). The solutions generated by these techniques were analyzed and validated by the help of cluster validity (CV) indices and later on their associations were determined with the obesity classes to discover the pattern from the obese sample. Two-Step clustering and hierarchical clustering outperformed the other applied techniques in identifying the subgroups based on the underlying hidden patterns in the data. Based on the CV indices values and the association analysis (obesity factor with the cluster solutions), two subgroups were generated and profiles of these groups have been reported. The first group belonged to the middle-aged individuals who seem to take care of their lifestyle while the other group belonged to young-aged individuals who in contrast to the first group presented a careless lifestyle factor (i.e., physical activity and dietary behavior). The salient features of these subgroups have been reported and can be proposed for the betterment in the health care industry. The research helped in identifying the interesting subsets/groups within survey data demonstrating similar characteristics and health status (i.e., prevalence of obesity with respect to lifestyle factors like physical activity, dietary behavior etc.) which will help to suggest appropriate measures/steps to be taken by the concerned departments to counter them and prevent in the population.

Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends

“…Furthermore, the technique does not address the issue of patient privacy when medical records are maintained in a centralized cloud database. Many research studies [132][133][134] propose using blockchain to store medical data in a distributed ledger to overcome the issue of a single point of failure.…”

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

“…The measurements, including anthropometric indices such as weight, height, and waist circumference, were taken. Blood pressure readings were also noted for all respondents using standard methodology [13], while individuals aged 20 years and above were additionally asked for biochemical measurements. Before the final data collection, a test run was carried out on the survey procedures and questionnaire to have standardized data collection [3,11,13,14].…”

“…Out of the total sample of 2184 records, 449 were filtered with 20.55% percent, and the required set of variables was chosen. A subset data set was chosen from the NHANSS data, whereas all the variables were included based on evidence-based research on obesity [7,13,18]. Since the obese sample had mixed variable types, the data type measurement for the variables was defined as quantitative and qualitative.…”

“…Like the other surveys, the NHANSS is a cross-sectional survey conducted among all age groups in all four districts. Figure 2 lists the details for data collection [7,10,13]. As represented, 67.70% of the data was collected from Brunei Muara, the most densely populated, 17% from Kuala Belait, 12% from Tutong, and 3.30% from Temburong, being the lowest among all.…”

A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

“…Clustering is an unsupervised method that does not require a prior definition of the input data classes. Clustering techniques help uncover hidden patterns in the examined data [1]. Unlike conventional clustering algorithms that process nonspatial or non-temporal data, clustering spatiotemporal data is challenging.…”