title: Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.
abstract: Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.
authors.affiliation: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan, Republic of China. ctsu@mx.nthu.edu.tw
authors.forename: Chao-Ton
authors.initials: CT
authors.lastname: Su
authors.forename: Pa-Chun
authors.initials: PC
authors.lastname: Wang
authors.forename: Yan-Cheng
authors.initials: YC
authors.lastname: Chen
authors.forename: Li-Fei
authors.initials: LF
authors.lastname: Chen
mesh_terms: D000368:Aged; D057225:Data Mining; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011182:Postoperative Care; D003668:Pressure Ulcer / Q000175:diagnosis*; D060388:Support Vector Machine