Using the machine learning approach to predict patient survival from high-dimensional survival data

Abstract

Survival analysis with high-dimensional data deals with the prediction of patient survival based on their gene expression data and clinical data. A crucial task for the accuracy of survival analysis in this context is to select the features highly correlated with the patient's survival time. Since the information about class labels is hidden, existing feature selection methods in machine learning are not applicable. In contrast to classical statistical methods which address this issue with the Cox score, we propose to tackle this problem by discretizing the survival time of patients into a suitable number of subgroups via silhouettes clustering validity. To cope with patients' censoring, we use “k-nearest neighbor” based on clinical parameters. Feature selection is then accomplished using Fast Correlation-Based Filtering approach from machine learning community. The effectiveness and efficiency of the proposed method are demonstrated through comparisons with classical statistical methods on real-world datasets and simulation datasets.

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