The application of data mining to explore association rules between metabolic syndrome and lifestyles.
This study used an efficient data mining algorithm, called DCIP (the data cutting and inner product method), to explore association rules between the lifestyles of factory workers in Taiwan and the metabolic syndrome. A total of 1,216 workers in four companies completed a lifestyle questionnaire. Results of the questionnaire survey were integrated into the workers' health examination reports to form an attribute database of the metabolic syndrome. Among the association rules derived by DCIP, 80% of those on the list of the top 15 highest support counts are corroborated by medical literature or by healthcare professionals. These findings prove that data mining is a valid and effective research method, and that larger sample sizes will likely produce more accurate associations connecting the metabolic syndrome to specific lifestyles. The rules already verified can serve as a reference guide for the health management of factory workers. The remaining 20%, while still lacking hard evidence, provide fertile ground for future research.
Department of Industrial Management National Pingtung University of Science and Technology, Taiwan Republic Of China.
Yi Chao
YC
Huang
D000328:Adult; D000465:Algorithms; D057225:Data Mining; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology*; D008875:Middle Aged; D015995:Prevalence; D012307:Risk Factors; D011795:Surveys and Questionnaires; D013624:Taiwan / Q000453:epidemiology