Deep learning based decision tree ensembles for incomplete medical datasets.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
Published Date: Jan 1, 2024

Abstract

BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose.

Authors

  • Chien-Hung Chiu
    Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, 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.
  • Wei-Chao Lin
    Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, Taiwan.
  • Min-Wei Huang
    Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chiayi, Taiwan.
  • Yi-Hsiu Ko
    Department of Information Management, National Central University, Taoyuan, Taiwan.

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