Risk classification and survival probability prediction are two major goals in survival data analysis because they play an important role in patients' risk stratification, long‐term diagnosis, and treatment selection. In this article, we propose a new model‐free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data and is therefore capable of capturing non‐linear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumour data and a breast cancer gene‐expression survival data are shown to illustrate the new methodology in real data analysis. Copyright © 2014 John Wiley & Sons, Ltd.

中文翻译：

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一种用于风险分类和生存概率预测的无模型机器学习方法。

风险分类和生存概率预测是生存数据分析的两个主要目标，因为它们在患者的风险分层，长期诊断和治疗选择中起着重要作用。在本文中，我们提出了一种新的无模型机器学习框架，用于基于加权支持向量机的风险分类和生存概率预测。新过程不需要对数据进行任何特定的参数或半参数模型假设，因此能够捕获非线性协变量效应。我们使用大量的仿真示例来证明所提出的方法在各种设置下的有限样本性能。显示了对神经胶质瘤肿瘤数据和乳腺癌基因表达生存数据的应用，以说明实际数据分析中的新方法。