Fırat University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Elazıg, Turkey
The goal of this study is to compare the performance of the deep survival model and the Cox regression model in an open-access Lung cancer dataset consisting of survivors and dead patients. In the study, it is applied to an open access dataset named “Lung Cancer Data” to compare the performances of the CPH and deepsurv models. The performance of the models is evaluated by C-index, AUC, and Brier score. The concordance index of the deep survival model is 0.64296, the Brier score was 0.128921, and the AUC was 0.6835. With the Cox regression model, the concordance index is calculated as 0.61445, brier score 0.1667, and AUC 0.5832. According to the Concordance index, brier score, and AUC criteria, the deep survival model performed better than the cox regression model. DeepSurv’s forecasting, modeling, and predictive capabilities pave the path for future deep neural network and survival analysis research. DeepSurv has the potential to supplement traditional survival analysis methods and become the standard method for medical doctors to examine and offer individualized treatment alternatives with more research.
Keywords: Cox regression, deep survival, survival, deep learning
Conflict of interests: The authors declare that there is no conflict of interest in the study.
Financial Disclosure: The authors declare that they have received no financial support for the study.
Ethical approval: There is no need for an informed consent form as the open source dataset is used in the study.
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Akbas KE, Balikci Cicek I, Kaya MO, Colak C .Comparison of Performance of Deep Survival and Cox Proportional
Hazard Models: an Application on the Lung Cancer Dataset. Med Science. 2022;11(3):1202-6.
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Corresponding Author: Kubra Elif Akbas, Fırat University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Elazig, Turkey.