Analysis of breast cancer using data mining & statistical techniques

Proceedings - Sixth Int. Conf. on Softw. Eng., Artificial Intelligence, Netw. and Parallel/Distributed Computing and First ACIS Int. Workshop on Self-Assembling Wireless Netw., SNPD/SAWN 2005

Volumn 2005, Issue , 2005, Pages 82-87

  Xiong, Xiangchun ^a   Kim, Yangon ^a   Baek, Yuncheol ^b   Rhee, Dae Wong ^b   Kim, Soo Hong ^b  

^a TOWSON UNIVERSITY   (United States)

^b Sangmyung University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BREAST CANCER; DECISION TREES; PLS LINEAR REGRESSION ANALYSIS; SURGICAL BIOPSY;

BIOPSY; HEALTH CARE; MAMMOGRAPHY; REGRESSION ANALYSIS; STATISTICAL METHODS; SURGERY; TREES (MATHEMATICS); TUMORS;

DATA MINING;

EID: 33749386201     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SNPD-SAWN.2005.19     Document Type: Conference Paper

References (6)

1. O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.
2. ftp://ftp.cs.wisc.edu/math-prog/cpo-dataset/machine-learn/cancer/
3. Ian H. Witten, Eibe Frank, Data Mining-Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers.
4. Eryi Zhu, Fanyuan Yang, Chemometrics Techniques and Applictions, Beijing Scientific Publishers.
5. Matthias Otto, Chemometrics: statistics and computer application in analytical chemistry / New York Weinheim Publishers.
6. Mehmed Kantardzic, Data Mining: Concepts, Models, Methods and Algorithms, Wiley Interscience.