TY - JOUR
T1 - Neural networks significantly improve cancer staging accuracy
AU - Burke, Harry
AU - Goodman, Philip
AU - Rosen, David
PY - 1994
Y1 - 1994
N2 - Cancer outcome prediction has been largely based on the pTNM staging system but this system presents two problems: (1) low accuracy rate and (2) no room for accuracy improvement because addition of predictive variables increases complexity. In this light, a comparison was made on the predictive accuracy of the current pTNM stage to a backpropagation neural network, for five-year breast cancer survival. This undertaking used the c-index as the measure of accuracy, which ranges from 0.5 (chance) to 1.0 (perfect prediction). Under the same variables, the pTNM stage system had a c-index of 0.69, while the backpropagation neural network scored 0.73, indicating better prediction accuracy.
AB - Cancer outcome prediction has been largely based on the pTNM staging system but this system presents two problems: (1) low accuracy rate and (2) no room for accuracy improvement because addition of predictive variables increases complexity. In this light, a comparison was made on the predictive accuracy of the current pTNM stage to a backpropagation neural network, for five-year breast cancer survival. This undertaking used the c-index as the measure of accuracy, which ranges from 0.5 (chance) to 1.0 (perfect prediction). Under the same variables, the pTNM stage system had a c-index of 0.69, while the backpropagation neural network scored 0.73, indicating better prediction accuracy.
UR - http://www.scopus.com/inward/record.url?scp=0028591550&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0028591550
SN - 1063-7125
JO - Proceedings of the IEEE Symposium on Computer-Based Medical Systems
JF - Proceedings of the IEEE Symposium on Computer-Based Medical Systems
T2 - Proceedings of the 1994 IEEE 7th Symposium on Computer-Based Medical Systems
Y2 - 11 June 1994 through 12 June 1994
ER -