期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 224, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2022.107026
关键词
Prognosis prediction; Breast cancer; Generative adversarial network; PregGAN
类别
资金
- National Natural Sci- ence Foundation of China [61771006]
- Natural Sci- ence Foundation of Henan Province [20230 0410 092, 20230 0410 093]
- Key scientific and technological project of Henan Province [222102310090]
- Postgraduate Education Re- form and Quality Improvement Project of Henan Province [YJS2022AL098]
This paper proposes a prognostic model PregGAN based on conditional generative adversarial network (CGAN) for disease prognosis prediction. Experimental results on breast cancer dataset show that PregGAN achieves good predictive performance and can be considered as a reliable prognostic model for breast cancer, as well as for other diseases.
Background and Objective: Generative adversarial network (GAN) is able to learn from a set of training data and generate new data with the same characteristics as the training data. Based on the characteristics of GAN, this paper developed its capability as a tool of disease prognosis prediction, and proposed a prognostic model PregGAN based on conditional generative adversarial network (CGAN).Methods: The idea of PregGAN is to generate the prognosis prediction results based on the clinical data of patients. PregGAN added the clinical data as conditions to the training process. Conditions were used as the input to the generator along with noises. The generator synthesized new samples using the noises vectors and the conditions. In order to solve the mode collapse problem during PregGAN training, Wasser-stein distance and gradient penalty strategy were used to make the training process more stable.Results: In the prognosis prediction experiments using the METABRIC breast cancer dataset, PregGAN achieved good results, with the average accurate (ACC) of 90.6% and the average AUC (area under curve) of 0.946.Conclusions: Experimental results show that PregGAN is a reliable prognosis predictive model for breast cancer. Due to the strong ability of probability distribution learning, PregGAN can also be used for the prognosis prediction of other diseases.(c) 2022 Elsevier B.V. All rights reserved.
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