Application of artificial neural network-based survival analysis on two breast cancer datasets.

Chih Lin Chi, W. Nick Street, William H. Wolberg

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

This paper applies artificial neural networks (ANNs) to the survival analysis problem. Because ANNs can easily consider variable interactions and create a non-linear prediction model, they offer more flexible prediction of survival time than traditional methods. This study compares ANN results on two different breast cancer datasets, both of which use nuclear morphometric features. The results show that ANNs can successfully predict recurrence probability and separate patients with good (more than five years) and bad (less than five years) prognoses. Results are not as clear when the separation is done within subgroups such as lymph node positive or negative.

Original languageEnglish (US)
Pages (from-to)130-134
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2007

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