On mining incomplete medical datasets: Ordering imputation and classification.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
Published Date: Jan 1, 2015

Abstract

BACKGROUND: To collect medical datasets, it is usually the case that a number of data samples contain some missing values. Performing the data mining task over the incomplete datasets is a difficult problem. In general, missing value imputation can be approached, which aims at providing estimations for missing values by reasoning from the observed data. Consequently, the effectiveness of missing value imputation is heavily dependent on the observed data (or complete data) in the incomplete datasets.

Authors

  • Chih-Wen Chen
    Department of Pharmacy, Kaohsiung Municipal Chinese Medical Hospital, Taiwan.
  • Wei-Chao Lin
    Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, Taiwan.
  • Shih-Wen Ke
    Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan.
  • Chih-Fong Tsai
    Department of Information Management, National Central University, Taiwan.
  • Ya-Han Hu
    Department of Information Management, National Chung Cheng University, Taiwan.

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