近年來由於國人飲食習慣及生活型態的改變,罹患疾病的型態轉變以慢性病為主,而代謝症候群是糖尿病、心血管疾病等慢性病罹患前的重要指標,本研究以某印刷電路板廠內40歲以上員工作為研究對象,整合員工健康檢查資料及生活型態問卷建立案例資料庫。針對案例資料庫運用關聯法則(Association Rules)進行資料探勘,挖掘代謝症候群與生活型態間之關聯性,所得探勘結果與與代謝症候群文獻進行驗證,可提供案例公司作為職場健康促進之參考。本研究利用資料切割排序法(Data Cutting & Sorting Method, DCSM)對Apriori演算法進行運算效能的改良,並利用標準資料庫做驗證,本研究證明資料切割排序演算法在支持度為0.02時較Apriori演算法的運算時間快了59.84倍,確定可降低探勘的時間成本並提升運算效能。
According to the alteration of diet habit and lifestyle, the chronic disease becomes the major disease in our country. The metabolic syndrome is an important index before people suffering from the chronic disease, such as diabetes, cardiovascular disease. In the researching, we took over 40-year-old employees as study object in a PCB company and built the database in terms of survey about their health check and lifestyle. We detected the relevance between metabolic syndrome and lifestyle by the case in the database mentioned above with association rules. The result after being verified with bibliography about metabolic syndrome can be applied to promote the occupational health for the sampled company. We took the advantage of Data Cutting & Sorting Method, DCSM to make advance in the computing performance of Apriori algorithm in the research. Through Verification with database, the performance time DCSM quicker 59.93 times than Apriori algorithm in support 0.02 condition.
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