Cox Model Improvement Using Residual Blocks in Neural Networks: A Study on the Predictive Model of Cervical Cancer Mortality
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Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, butconsidering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paperproposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learningframework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models comparedin this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models comparedin this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment inthe management of cervical cancer patients and represents significant progress in the field of survival analysis.
Abstract
Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, butconsidering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paperproposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learningframework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models comparedin this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models comparedin this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment inthe management of cervical cancer patients and represents significant progress in the field of survival analysis.
참고문헌 (22)
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