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.
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The authors feel much obligated for the time spent to review this paper by highly informed the referees.
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Mokarram, R., Emadi, M. Classification in Non-linear Survival Models Using Cox Regression and Decision Tree. Ann. Data. Sci. 4, 329–340 (2017). https://doi.org/10.1007/s40745-017-0105-4
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DOI: https://doi.org/10.1007/s40745-017-0105-4