TY  - JOUR
TI  - Comparison of Performance of Deep Survival and Cox Proportional  Hazard Models: an Application on the Lung Cancer Dataset
AB  - 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 survi vors 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 Con cordance 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.
AU  - AKBAŞ, Kübra Elif
AU  - ÇOLAK, Cemil
AU  - BALIKCI CICEK, IPEK
AU  - KAYA, Mehmet Onur
DO  - 10.5455/medscience.2022.03.078
PY  - 2022
JO  - Medicine Science
VL  - 11
IS  - 3
SN  - 2147-0634
SP  - 1202
EP  - 1206
DB  - TRDizin
UR  - http://search/yayin/detay/1131319
ER  -