Comparing artificial neural networks to other statistical methods for medical outcome prediction

Harry B. Burke; David B. Rosen; Philip H. Goodman

Comparing artificial neural networks to other statistical methods for medical outcome prediction

Harry B. Burke^*, David B. Rosen, Philip H. Goodman

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

25 Scopus citations

Abstract

When compared to the TNM stage system, neural networks are able to significantly improve breast cancer outcome prediction accuracy. They can combine prognostic factors to further improve accuracy. Neural networks are robust across data bases and cancer sites. Neural networks can perform as well as the best conventional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex genetic interactions.

Original language	English
Pages	2213-2216
Number of pages	4
State	Published - 1994
Externally published	Yes
Event	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: 27 Jun 1994 → 29 Jun 1994

Conference

Conference	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
City	Orlando, FL, USA
Period	27/06/94 → 29/06/94

Cite this

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title = "Comparing artificial neural networks to other statistical methods for medical outcome prediction",

abstract = "When compared to the TNM stage system, neural networks are able to significantly improve breast cancer outcome prediction accuracy. They can combine prognostic factors to further improve accuracy. Neural networks are robust across data bases and cancer sites. Neural networks can perform as well as the best conventional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex genetic interactions.",

author = "Burke, {Harry B.} and Rosen, {David B.} and Goodman, {Philip H.}",

year = "1994",

language = "English",

pages = "2213--2216",

note = "Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) ; Conference date: 27-06-1994 Through 29-06-1994",

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T1 - Comparing artificial neural networks to other statistical methods for medical outcome prediction

AU - Burke, Harry B.

AU - Rosen, David B.

AU - Goodman, Philip H.

PY - 1994

Y1 - 1994

N2 - When compared to the TNM stage system, neural networks are able to significantly improve breast cancer outcome prediction accuracy. They can combine prognostic factors to further improve accuracy. Neural networks are robust across data bases and cancer sites. Neural networks can perform as well as the best conventional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex genetic interactions.

AB - When compared to the TNM stage system, neural networks are able to significantly improve breast cancer outcome prediction accuracy. They can combine prognostic factors to further improve accuracy. Neural networks are robust across data bases and cancer sites. Neural networks can perform as well as the best conventional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex genetic interactions.

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M3 - Paper

AN - SCOPUS:0028733766

SP - 2213

EP - 2216

T2 - Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)

Y2 - 27 June 1994 through 29 June 1994

ER -

Comparing artificial neural networks to other statistical methods for medical outcome prediction

Abstract

Conference

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