The Application of Data Mining to Explore Association Rules between Metabolic Syndrome and Lifestyles

Abstract

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.

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

“…From the methodological perspective, different ARM methods have been employed in the literature. Some of those methods include data cutting and inner product (DCIP) method, 33,34 extended FP-growth methods, 35 Boolean analyzer, 29 and Tertius. 27 However, many of the research works used Apriori to discover association rules in medical data sets, 16,22,27 which makes ARM one of the popular and widely used methods without doubt.…”

Diagnosis of Metabolic Syndrome using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

“…Huang [75] applied an association rules analysis (AA) algorithm called the data cutting and inner product (DCIP) method in order to investigate the association between MetSR-F and the risk of MetS in factory workers. DCIP partitions, sorts and carries out inner production operations on data in order to speed up the data mining process and improve computation efficiency.…”

Data mining for the identification of metabolic syndrome status

“…Furthermore, this technique was used to explore association rules between MS and lifestyle (Huang, 2013[ 10 ]). It was found that individuals having a BMI >27 kg/m 2 and/or participating in vigorous physical exercise less than once a week were predisposed to having MS.…”

“…A summary of examples employing data mining for the classification of MS is presented in Table 5 (Tab. 5) (References in Table 5: de Edelenyi et al, 2008[ 6 ]; Karimi-Alavijeh et al, 2016[ 13 ]; Kim et al, 2012[ 14 ]; Chan et al, 2008[ 4 ]; Huang, 2013[ 10 ]; Lin et al, 2010[ 20 ]; Worachartcheewan et al, 2010[ 46 ], 2013[ 48 ], 2015[ 49 ]; Miller et al, 2014[ 22 ]). It was used to identify patterns or combinations of MS components as well as to deduce rules for metabolic abnormalities associated with MS.…”