Feature selection for medical data mining: Comparisons of expert judgment and automatic approaches

Tsang Hsiang Cheng*, Chih Ping Wei, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Scopus citations

Abstract

Data mining refers to the process of automatic extracting previously unknown, valid, and actionable patterns or knowledge from large databases for crucial decision support. Among different data mining technique, classification analysis is widely adopted for Healthcare applications for supporting medical diagnostic decisions, improving quality of patient care, etc. If a training dataset contains irrelevant features (i.e., attributes), classification analysis may produce less accurate and less understandable results. Two commonly employed feature selection approaches include use of automatic feature selection mechanisms (i.e., data-driven) or expert judgment (i.e., knowledge-driven). Due to differences in their underlying processes, the two prevailing feature selection approaches may have their unique biases that possibly lead to dissimilar classification effectiveness. In this study, we empirically evaluate the classification effectiveness resulted from the two feature selection approaches on a risk prediction of cardiovascular disease dataset. Our evaluation results suggest that the feature subsets selected domain experts improve the sensitivity of a classifier, while the feature subsets selected by an automatic feature selection mechanism improve the predictive power of a classifier on the majority class (i.e., the specificity in this study).

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006
Pages165-170
Number of pages6
DOIs
StatePublished - 22 Dec 2006
Event19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006 - Salt Lake City, UT, United States
Duration: 22 Jun 200623 Jun 2006

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2006
ISSN (Print)1063-7125

Conference

Conference19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006
CountryUnited States
CitySalt Lake City, UT
Period22/06/0623/06/06

Keywords

  • Atherosclerosis
  • Classification analysis
  • Expert judgment
  • Feature selection
  • Medical data mining

Fingerprint Dive into the research topics of 'Feature selection for medical data mining: Comparisons of expert judgment and automatic approaches'. Together they form a unique fingerprint.

  • Cite this

    Cheng, T. H., Wei, C. P., & Tseng, S. (2006). Feature selection for medical data mining: Comparisons of expert judgment and automatic approaches. In Proceedings - 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006 (pp. 165-170). [1647563] (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2006). https://doi.org/10.1109/CBMS.2006.87