Application of bayesian network classifiers and data envelopment analysis for mining breast cancer patterns

Parag C. Pendharkar, Mehdi Khosrowpour, James A. Rodger

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Data envelopment analysis (DEA) and learning Bayesian networks (LBN) are relatively unexplored approaches for data mining. Recent theoretical research indicates that DEA and LBN can be used for certain data mining applications. We apply DEA and LBN for discovering the breast cancer pattern. We use data from a large hospital in Pennsylvania to discover the breast cancer patterns, and benchmark the performance of DEA and LBN against a popular statistical linear discriminant analysis technique. The results of our experiments indicate that DEA and LBN outperform statistical linear discriminant analysis.

Original languageEnglish (US)
Pages (from-to)127-131
Number of pages5
JournalJournal of Computer Information Systems
Volume40
Issue number4
StatePublished - Jun 1 2000

Fingerprint

Data envelopment analysis
Bayesian networks
data analysis
cancer
Classifiers
learning
discriminant analysis
Discriminant analysis
Data mining
experiment
performance
Experiments

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Education
  • Computer Networks and Communications

Cite this

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Application of bayesian network classifiers and data envelopment analysis for mining breast cancer patterns. / Pendharkar, Parag C.; Khosrowpour, Mehdi; Rodger, James A.

In: Journal of Computer Information Systems, Vol. 40, No. 4, 01.06.2000, p. 127-131.

Research output: Contribution to journalArticle

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