Classification in Non-linear Survival Models Using Cox Regression and Decision Tree

Reza Mokarram and Mehdi Emadi ()
Additional contact information
Reza Mokarram: Ferdowsi University of Mashhad
Mehdi Emadi: Ferdowsi University of Mashhad

Annals of Data Science, 2017, vol. 4, issue 3, No 2, 329-340

Abstract: Abstract Classification is the most important issues that have gained much attention in various fields such as health and medicine. Especially in survival models, classification represents a main objective and it is also one of the main purposes in data mining. Among data mining methods used for classification, implementation of the decision tree due to its simplicity and understandable and accurate results, has gained much attention and popularity. In this paper, first we generate the observations by using Monte-Carlo simulation from hazard model with the three degrees of complexity in different levels of censorship 0 to 70%. Then the accuracy of classification in the Cox and the decision tree models is compared for the number of samples 1000, 5000 and 10,000 by area under the ROC curve(AUC) and the ROC-test.

Keywords: Proportional hazard; Cox model; Decision tree; Classification; Non-linear survival model; Non-Parametric model (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-017-0105-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:4:y:2017:i:3:d:10.1007_s40745-017-0105-4

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-017-0105-4

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().