Survival data is common in medical applications. The challenge in applying predictive data-analytic methods to survival data is in the treatment of censored observations, since the survival times for these observations are unknown. This paper presents formalization of the analysis of survival data as a binary classification problem. For this binary classification setting, we propose a strategy for encoding censored data, leading to the SVM/LUPI formulations. Further, we present empirical comparison of the new method and the classical Cox modeling approach for predictive modeling of survival data. These comparisons suggest that for data sets with large amount of censored data, the proposed method consistently yields better predictive performance than classical statistical modeling.
|Original language||English (US)|
|Title of host publication||Proceedings of the International Joint Conference on Neural Networks|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Sep 3 2014|
|Event||2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China|
Duration: Jul 6 2014 → Jul 11 2014
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Other||2014 International Joint Conference on Neural Networks, IJCNN 2014|
|Period||7/6/14 → 7/11/14|
Bibliographical notePublisher Copyright:
© 2014 IEEE.