Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Fulden CANTAŞ TÜRKİŞ, İ. Kurt Omurlu, M. Türe
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引用次数: 0

Abstract

Mortality risks of important diseases such as cancer can be estimated using gene profiles which are high-dimensional data obtained from gene expression sequences. However, it is impossible to analyze high-dimensional data with classical techniques due to multicollinearity, time-consuming processing load, and difficulty interpreting the results. For this purpose, extreme learning machine methods, which can solve regression and classification problems, have become one of the most preferred machine learning methods regarding fast data analysis and ease of application. The goal of this study is to compare estimation performance of risk score and short-term survival with survival extreme learning machine methods, L2-penalty Cox regression, and supervised principal components analysis in generated high-dimensional survival data. The survival models have been evaluated by Harrell’s concordance index, integrated Brier score, F1 score, kappa coefficient, the area under the curve, the area under precision-recall, accuracy, and Matthew’s correlation coefficient. All results showed that survival extreme learning machine methods that allow analyzing high-dimensional survival data without the necessity of dimension reduction perform very competitive with the other popular classical methods used in the study.
基于极端学习机的高维生存数据生存预测、监督主成分和正则化Cox模型的仿真研究
癌症等重要疾病的死亡风险可以利用基因谱来估计,基因谱是由基因表达序列获得的高维数据。然而,由于多重共线性、费时的处理负荷和难以解释结果,传统技术无法分析高维数据。为此,能够解决回归和分类问题的极限学习机器方法因其快速分析数据和易于应用而成为最受青睐的机器学习方法之一。本研究的目的是比较在生成的高维生存数据中,使用生存极限学习机方法、l2惩罚Cox回归和监督主成分分析对风险评分和短期生存的估计性能。采用Harrell’s concordance index、综合Brier评分、F1评分、kappa系数、曲线下面积、查准率-查全率下面积、准确率和Matthew’s相关系数对生存模型进行评价。所有结果表明,生存极限学习机方法允许分析高维生存数据,而不需要降维,与研究中使用的其他流行的经典方法相比,表现得非常有竞争力。
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来源期刊
gazi university journal of science
gazi university journal of science MULTIDISCIPLINARY SCIENCES-
CiteScore
1.60
自引率
11.10%
发文量
87
期刊介绍: The scope of the “Gazi University Journal of Science” comprises such as original research on all aspects of basic science, engineering and technology. Original research results, scientific reviews and short communication notes in various fields of science and technology are considered for publication. The publication language of the journal is English. Manuscripts previously published in another journal are not accepted. Manuscripts with a suitable balance of practice and theory are preferred. A review article is expected to give in-depth information and satisfying evaluation of a specific scientific or technologic subject, supported with an extensive list of sources. Short communication notes prepared by researchers who would like to share the first outcomes of their on-going, original research work are welcome.
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