关键词: 生存时间预测, 二次监督学习风范, K最近邻算法, 支持向量机回归

Abstract: The survival time prediction has been applied on numbers of fields such as clinic medicine, economy and engineering. With the development and broad application of machine learning technology and data mining, lots of survival time prediction models and algorithms have been proposed. Although some have achieved good outcomes, the prediction accuracy still has much space for improvement. This paper proposes a survival time prediction algorithm based on twice supervised learning style. It also realizes the modeling of survival time through both the application of nearest-neighbor algorithm utilized for censored samples estimation and the feature of generalization property of support vector machine (SVM). The experimental results show that the proposed algorithm can achieve precise survival time prediction in clinic, and take the advantage of performance on accurate prognosis.

Key words: survival time prediction, twice supervised learning style, K-nearest neighbor algorithm, support vector machine regression