Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry.
Construction accident research involves the systematic sorting, classification, and encoding of comprehensive databases of injuries and fatalities. The present study explores the causes and distribution of occupational accidents in the Taiwan construction industry by analyzing such a database using the data mining method known as classification and regression tree (CART). Utilizing a database of 1542 accident cases during the period 2000-2009, the study seeks to establish potential cause-and-effect relationships regarding serious occupational accidents in the industry. The results of this study show that the occurrence rules for falls and collapses in both public and private project construction industries serve as key factors to predict the occurrence of occupational injuries. The results of the study provide a framework for improving the safety practices and training programs that are essential to protecting construction workers from occasional or unexpected accidents.
Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan District, New Taipei City 243, Taiwan.
Ching-Wu
CW
Cheng
Sou-Sen
SS
Leu
Ying-Mei
YM
Cheng
Tsung-Chih
TC
Wu
Chen-Chung
CC
Lin
D000062:Accidents, Occupational / Q000517:prevention & control / Q000706:statistics & numerical data*; D000328:Adult; D016009:Chi-Square Distribution; D060389:Construction Industry / Q000592:standards; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D060051:Occupational Injuries / Q000453:epidemiology / Q000209:etiology* / Q000517:prevention & control; D012044:Regression Analysis; D017751:Safety Management; D013624:Taiwan / Q000453:epidemiology