Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.

生存风险预测模型已成为临床医生改进癌症治疗决策的重要工具。在医学领域，使用基因表达数据构建深度生存神经网络模型可显著提高准确的生存预后。然而，对于构建一种有效的方法来提高癌症特异性生存风险预测的准确性，例如数据噪声问题，它仍然构成了挑战。为了解决上述问题，我们提出了一种 grid optimization （DRGONet） 的 diversity reweighted 深度生存神经网络工作方法，以提高癌症特异性生存风险预测的准确性。具体来说，可以采用重新加权来根据数据点的重要性或相关性调整分配给数据集中每个数据点的权重，从而减轻嘈杂或不相关数据的影响并提高模型性能。将多样性纳入多种学习模式的目标有助于最大限度地减少偏见并改善学习成果。此外，超参数可以通过网格优化进行优化。实验结果表明，我们提出的方法在现实世界的医疗场景中具有显着优势（提高了约 5%），大大优于最先进的比较方法。我们的研究强调了使用 DRGONet 克服构建准确生存预测模型的局限性的重要性。通过在癌症研究中应用我们的技术，我们希望减少癌症患者所经历的痛苦并提高治疗效果。