Yıl: 2022 Cilt: 11 Sayı: 3 Sayfa Aralığı: 1202 - 1206 Metin Dili: İngilizce DOI: 10.5455/medscience.2022.03.078 İndeks Tarihi: 12-10-2022

Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset

Öz:
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.
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  • Kartsonaki C. Survival analysis. Diagnostic Histopathology. 2016;22:263- 70.
  • Knox KL, Bajorska A, Feng C, et al. Survival analysis for observational and clustered data: an application for assessing individual and environmental risk factors for suicide. Shanghai Arch Psychiatry. 2013;25:183.
  • Thammasorn P, Schaub SK, Hippe DS, et al. Regularizing the deepsurv network using projection loss for med risk assessment. IEEE Access. 2022;10:8005-20.
  • Katzman JL, Shaham U, Cloninger A, et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:1-12.
  • Katzman JL, Shaham U, Cloninger A, et al. Deep survival: A deep cox proportional hazards network. Stat. 2016;1050:1-10.
  • Wang L, Li Y, Chignell M. Combining ranking and point-wise losses for training deep survival analysis models. IEEE International Conference on Data Mining (ICDM). 2021;689-98.
  • Yu H, Huang T, Feng B, Lyu J. Deep-learning Model for predicting the survival of rectal adenocarcinoma patients based on the seer. database 2021
  • Shu M, Bowen RS, Herrmann C, et al. Deep survival analysis with longitudinal X-rays for COVID-19. Proceedings of the IEEE/CVF Int Conference Computer Vision. 2021:4046-55.
  • https://www.kaggle.com/code/saychakra/survival-analysis-of-lung-cancer patients/data
  • ALPAR R. Applied multivariate statistical methods. 5th Edition Detay Publishing House, Ankara, 2017; 400.
  • Cox DR. Regression models and life tables. Journal of the Royal Statistical Society. 1972;34:187-202.
  • Ozdamar K. Biostatistics with SPSS. 4th Edition. Kaan Publishing House, Eskişehir, 2001.
  • Garcia FCC, Hirao A, Tajika A, et al. Leveraging longitudinal lifelog data using survival models for predicting risk of relapse among patients with depression in remission. Annual Int Conf IEEE Eng Med Biol Soc. 2021:2455-8.
  • Faraggi D, Simon R. A neural network model for survival data. Stat Med. 1995;14:73-82.
  • Kvamme, H., Borgan, Ø., & Scheel, I. Time-to-event prediction with neural networks and Cox regression. JMLR. 2019;1-30.
  • Englebert C, Quinn T, Bichindaritz I. Feature selection for survival analysis in bioinformatics. in proceedings of the workshop on advances in bioinformatics and artificial intelligence: bridging the gap co-located with 26th International Joint Conference on Artificial Intelligence (IJCAI). 2017;30-5.
  • Harrell Jr FE, Lee KL, Califf RM, et al. Regression modelling strategies for improved prognostic prediction. Stat Med. 1984;3:143-52.
  • Zhao L, Feng D. Deep neural networks for survival analysis using pseudo values. IEEE J Biomed Health Inform. 2020;24:3308-14.
  • Oei RW, Lyu Y, Ye L, et al. Progression-free survival prediction in patients with nasopharyngeal carcinoma after intensity-modulated radiotherapy: machine learning vs. traditional statistics. J Pers Med. 2021;11:787.
  • Lutz M. Programming python: O'Reilly Media, Inc; 2001.
  • Sertkaya D, Nihal A, Sozer MT. Cox regression model with time dependent covariate in survival anaysis. Journal of Ankara University Faculty Medicine. 2005;58:153-8.
  • Zhu X, Yao J, Huang J, et al. Deep convolutional neural network for survival analysis with pathological images. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2016:544-7.
APA AKBAŞ K, KAYA M, BALIKCI CICEK I, ÇOLAK C (2022). Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. , 1202 - 1206. 10.5455/medscience.2022.03.078
Chicago AKBAŞ Kübra Elif,KAYA Mehmet Onur,BALIKCI CICEK IPEK,ÇOLAK Cemil Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. (2022): 1202 - 1206. 10.5455/medscience.2022.03.078
MLA AKBAŞ Kübra Elif,KAYA Mehmet Onur,BALIKCI CICEK IPEK,ÇOLAK Cemil Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. , 2022, ss.1202 - 1206. 10.5455/medscience.2022.03.078
AMA AKBAŞ K,KAYA M,BALIKCI CICEK I,ÇOLAK C Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. . 2022; 1202 - 1206. 10.5455/medscience.2022.03.078
Vancouver AKBAŞ K,KAYA M,BALIKCI CICEK I,ÇOLAK C Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. . 2022; 1202 - 1206. 10.5455/medscience.2022.03.078
IEEE AKBAŞ K,KAYA M,BALIKCI CICEK I,ÇOLAK C "Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset." , ss.1202 - 1206, 2022. 10.5455/medscience.2022.03.078
ISNAD AKBAŞ, Kübra Elif vd. "Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset". (2022), 1202-1206. https://doi.org/10.5455/medscience.2022.03.078
APA AKBAŞ K, KAYA M, BALIKCI CICEK I, ÇOLAK C (2022). Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. Medicine Science, 11(3), 1202 - 1206. 10.5455/medscience.2022.03.078
Chicago AKBAŞ Kübra Elif,KAYA Mehmet Onur,BALIKCI CICEK IPEK,ÇOLAK Cemil Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. Medicine Science 11, no.3 (2022): 1202 - 1206. 10.5455/medscience.2022.03.078
MLA AKBAŞ Kübra Elif,KAYA Mehmet Onur,BALIKCI CICEK IPEK,ÇOLAK Cemil Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. Medicine Science, vol.11, no.3, 2022, ss.1202 - 1206. 10.5455/medscience.2022.03.078
AMA AKBAŞ K,KAYA M,BALIKCI CICEK I,ÇOLAK C Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. Medicine Science. 2022; 11(3): 1202 - 1206. 10.5455/medscience.2022.03.078
Vancouver AKBAŞ K,KAYA M,BALIKCI CICEK I,ÇOLAK C Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset. Medicine Science. 2022; 11(3): 1202 - 1206. 10.5455/medscience.2022.03.078
IEEE AKBAŞ K,KAYA M,BALIKCI CICEK I,ÇOLAK C "Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset." Medicine Science, 11, ss.1202 - 1206, 2022. 10.5455/medscience.2022.03.078
ISNAD AKBAŞ, Kübra Elif vd. "Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset". Medicine Science 11/3 (2022), 1202-1206. https://doi.org/10.5455/medscience.2022.03.078