On the application of clustering and classification techniques to analyze metabolic syndrome severity distribution area and critical factors

Abstract

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.