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With the continuous development of human life and society, the medical field is constantly improving. However, modern medicine still faces many limitations, including challenging and previously unsolvable problems. In these cases, artificial intelligence (AI) can provide solutions. The research and application of generative adversarial networks (GAN) are a clear example. While most researchers focus on image augmentation, there are few one-dimensional data augmentation examples. The radiomics feature extracted from RT and CT images is one-dimensional data. As far as we know, we are the first to apply the WGAN-GP algorithm to generate radiomics data in the medical field. In this paper, we input a portion of the original real data samples into the model. The model learns the distribution of the input data samples and generates synthetic data samples with similar distribution to the original real data, which can solve the problem of obtaining annotated medical data samples. We have conducted experiments on the public dataset Heart Disease Cleveland and the private dataset. Compared with the traditional method of Synthetic Minority Oversampling Technique (SMOTE) and common GAN for data augmentation, our method has significantly improved the AUC and SEN values under different data proportions. At the same time, our method has also shown varying levels of improvement in ACC and SPE values. This demonstrates that our method is effective and feasible. [ABSTRACT FROM AUTHOR]