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Data Mining Techniques for Assisting the Diagnosis of Pressure Ulcer Development in Surgical Patients

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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, F1, 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.

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Acknowledgement

This work was supported in part by the National Science Council, Taiwan, under grant NSC-98-2221-E-007-071-MY3

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Authors and Affiliations

  1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Room 820, Engineering Building I, 101, Sec. 2, Kuang Fu Rd., Hsinchu, 30013, Taiwan, Republic of China

    Chao-Ton Su

  2. School of Medicine, Fu-Jen Catholic University, No.510, Zhongzheng Rd., Xinzhung Dist., New Taipei City, 24205, Taiwan, Republic of China

    Pa-Chun Wang

  3. Department of Otolaryngology, Cathay General Hospital, No. 280, Sec. 4, Renai Rd., Taipei, Taiwan, Republic of China

    Pa-Chun Wang

  4. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Room 708, Engineering Building I, 101, Sec. 2, Kuang Fu Rd., Hsinchu, 30013, Taiwan, Republic of China

    Yan-Cheng Chen

  5. Graduate Institute of Business Administration, Fu-Jen Catholic University, No.510, Zhongzheng Rd., Xinzhung Dist., New Taipei City, 24205, Taiwan, Republic of China

    Li-Fei Chen

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  1. Chao-Ton Su
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  2. Pa-Chun Wang
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Correspondence to Chao-Ton Su.

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Su, CT., Wang, PC., Chen, YC. et al. Data Mining Techniques for Assisting the Diagnosis of Pressure Ulcer Development in Surgical Patients. J Med Syst 36, 2387–2399 (2012). https://doi.org/10.1007/s10916-011-9706-1

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  • DOI: https://doi.org/10.1007/s10916-011-9706-1

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