Next Article in Journal
Reply to Comment on Di Marco, N., Kaufman, J., Rodda, C.P. Shedding Light on Vitamin D Status and Its Complexities during Pregnancy, Infancy and Childhood: An Australian Perspective. Int. J. Environ. Res. Public Health 2019, 16 (4), 538, doi:10.3390/ijerph16040538
Previous Article in Journal
Associations between Polypharmacy, Self-Rated Health, and Depression in African American Older Adults; Mediators and Moderators
Article

On the Application of Clustering and Classification Techniques to Analyze Metabolic Syndrome Severity Distribution Area and Critical Factors

Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei 24301, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(9), 1575; https://doi.org/10.3390/ijerph16091575
Received: 27 February 2019 / Revised: 26 April 2019 / Accepted: 3 May 2019 / Published: 6 May 2019
In recent years, metabolic syndrome has become one of the leading causes of death in Taiwan. This study proposes a classification and clustering method specific to the administrative regions of New Taipei City to explore the incidence and corresponding risk factors for metabolic syndrome in various geographic areas. We used integrated community health screening data and survey results obtained from people aged ≥40 years in each of the administrative regions of New Taipei City as study samples. Using a combination of Ward’s method, multivariate analysis of variance, and k-means, we identified administrative regions of New Taipei City with metabolic syndrome incidences of a similar nature. Classification and regression tree methods were used to discover the key causes of metabolic syndrome in each region based on lifestyles and dietary habits. The administrative regions were divided into four groups: high-risk, slightly high-risk, normal-risk, and low-risk. The results showed that the severity of metabolic syndrome varies by region and the risk factors for metabolic syndrome vary by region. It has also been found that regions with a higher incidence of metabolic syndrome have relatively fewer medical resources. View Full-Text
Keywords: metabolic syndrome; integrated community health screening; decision trees metabolic syndrome; integrated community health screening; decision trees
Show Figures

Figure 1

MDPI and ACS Style

Wang, C.-C.; Jhu, J.-J. On the Application of Clustering and Classification Techniques to Analyze Metabolic Syndrome Severity Distribution Area and Critical Factors. Int. J. Environ. Res. Public Health 2019, 16, 1575. https://doi.org/10.3390/ijerph16091575

AMA Style

Wang C-C, Jhu J-J. On the Application of Clustering and Classification Techniques to Analyze Metabolic Syndrome Severity Distribution Area and Critical Factors. International Journal of Environmental Research and Public Health. 2019; 16(9):1575. https://doi.org/10.3390/ijerph16091575

Chicago/Turabian Style

Wang, Chien-Chih, and Jin-Jiang Jhu. 2019. "On the Application of Clustering and Classification Techniques to Analyze Metabolic Syndrome Severity Distribution Area and Critical Factors" International Journal of Environmental Research and Public Health 16, no. 9: 1575. https://doi.org/10.3390/ijerph16091575

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop