Review of data mining approaches for extraction and classification of clinical data in diagnosis of coronary artery disease

Noreen Kausar*, Azween Abdullah, Brahim Belhaouari Samir, Sellapan Palaniappan, Bandar Saeed Alghamdi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Coronary artery disease (CAD) has been ranked as the top cause of death by world health organization in many countries especially Asia. In Malaysia, 22.18% of total deaths are caused by CAD. In this paper, our focus is to review possible types of data mining algorithms applied for processing of clinical attributes as well as their classification to identify normal and CAD patients in minimal time with optimized accuracy. Various combinations of these techniques and variation have adverse effects as well as increased performance, which will be covered in this paper. Data selection for designing a detection system also varies the system performance and it can be dealt with using standard data sets with relevant feature to ease detection of abnormalities with maximum detection rate.

Original languageEnglish
Pages (from-to)6679-6685
Number of pages7
JournalARPN Journal of Engineering and Applied Sciences
Volume10
Issue number15
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Classification
  • Cleveland dataset
  • Coronary artery disease (CAD)
  • Feature processing
  • Irvine (UCI)
  • Supervised learning
  • University of california

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