Association, statistical, mathematical and neural approaches for mining breast cancer patterns

P. C. Pendharkar; J. A. Rodger; G. J. Yaverbaum; N. Herman; M. Benner

doi:10.1016/s0957-4174(99)00036-6

Association, statistical, mathematical and neural approaches for mining breast cancer patterns

P. C. Pendharkar, J. A. Rodger, G. J. Yaverbaum, N. Herman, M. Benner

School of Business Administration (Harrisburg)

Research output: Contribution to journal › Article › peer-review

90 Scopus citations

Abstract

Using several association and classification approaches to study breast cancer patterns, this study illustrates how these approaches can be used to predict and diagnose the occurrence of breast cancer. The results of the study, based on data obtained from a large medical facility in western Pennsylvania, show that data mining can be a viable tool for breast cancer diagnosis.

Original language	English (US)
Pages (from-to)	223-232
Number of pages	10
Journal	Expert Systems With Applications
Volume	17
Issue number	3
DOIs	https://doi.org/10.1016/s0957-4174(99)00036-6
State	Published - 1999

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/s0957-4174(99)00036-6

Cite this

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title = "Association, statistical, mathematical and neural approaches for mining breast cancer patterns",

abstract = "Using several association and classification approaches to study breast cancer patterns, this study illustrates how these approaches can be used to predict and diagnose the occurrence of breast cancer. The results of the study, based on data obtained from a large medical facility in western Pennsylvania, show that data mining can be a viable tool for breast cancer diagnosis.",

author = "Pendharkar, {P. C.} and Rodger, {J. A.} and Yaverbaum, {G. J.} and N. Herman and M. Benner",

note = "Funding Information: The research done in this study was funded by a research grant from University of Pittsburgh.",

year = "1999",

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AU - Pendharkar, P. C.

AU - Rodger, J. A.

AU - Yaverbaum, G. J.

AU - Herman, N.

AU - Benner, M.

N1 - Funding Information: The research done in this study was funded by a research grant from University of Pittsburgh.

PY - 1999

Y1 - 1999

N2 - Using several association and classification approaches to study breast cancer patterns, this study illustrates how these approaches can be used to predict and diagnose the occurrence of breast cancer. The results of the study, based on data obtained from a large medical facility in western Pennsylvania, show that data mining can be a viable tool for breast cancer diagnosis.

AB - Using several association and classification approaches to study breast cancer patterns, this study illustrates how these approaches can be used to predict and diagnose the occurrence of breast cancer. The results of the study, based on data obtained from a large medical facility in western Pennsylvania, show that data mining can be a viable tool for breast cancer diagnosis.

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U2 - 10.1016/s0957-4174(99)00036-6

DO - 10.1016/s0957-4174(99)00036-6

M3 - Article

AN - SCOPUS:0000532568

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ER -

Association, statistical, mathematical and neural approaches for mining breast cancer patterns

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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