Second Case Study: Inference of Diagnostic Rules for Breast Cancer

Evangelos Triantaphyllou ()
Additional contact information
Evangelos Triantaphyllou: Louisiana State University

Chapter Chapter 15 in Data Mining and Knowledge Discovery via Logic-Based Methods, 2010, pp 289-296 from Springer

Abstract: Abstract For this case study we used a data set that described a number of clinical cases of breast cancer diagnoses. The data were divided into two disjoint sets of malignant and sub benign case benign cases. We applied the sub OCAT (one clause at a time) approach OCAT approach, as it is embedded in the RA1 heuristic (see also Chapter 4), after the data were transformed into binary ones according to the method described in Section 2.2. The following sections describe the data and inferred sub diagnostic rules diagnostic rules in more detail.

Keywords: Breast Cancer; Baton Rouge; Breast Cancer Data; Benign Case; Intraductal Carcinoma (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4419-1630-3_15

Ordering information: This item can be ordered from
http://www.springer.com/9781441916303

DOI: 10.1007/978-1-4419-1630-3_15

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().