Cox Model Improvement Using Residual Blocks in Neural Networks: A Study on the Predictive Model of Cervical Cancer Mortality 


Vol. 13,  No. 6, pp. 260-268, Jun.  2024
https://doi.org/10.3745/TKIPS.2024.13.6.260


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  Abstract

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

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[IEEE Style]

N. K. Lee, J. Y. Kim, J. S. Tak, H. R. Lee, H. J. Jeon, J. M. Yang, S. W. Lee, "Cox Model Improvement Using Residual Blocks in Neural Networks: A Study on the Predictive Model of Cervical Cancer Mortality," The Transactions of the Korea Information Processing Society, vol. 13, no. 6, pp. 260-268, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.6.260.

[ACM Style]

Nang Kyeong Lee, Joo Young Kim, Ji Soo Tak, Hyeong Rok Lee, Hyun Ji Jeon, Jee Myung Yang, and Seung Won Lee. 2024. Cox Model Improvement Using Residual Blocks in Neural Networks: A Study on the Predictive Model of Cervical Cancer Mortality. The Transactions of the Korea Information Processing Society, 13, 6, (2024), 260-268. DOI: https://doi.org/10.3745/TKIPS.2024.13.6.260.

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