statistical learning methods as a preprocessing step for survival analysis evaluation of concept using lung cancer data统计学习方法作为生存分析的预处理步骤的评估使用肺癌数据概念.pdfVIP

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statistical learning methods as a preprocessing step for survival analysis evaluation of concept using lung cancer data统计学习方法作为生存分析的预处理步骤的评估使用肺癌数据概念.pdf

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statistical learning methods as a preprocessing step for survival analysis evaluation of concept using lung cancer data统计学习方法作为生存分析的预处理步骤的评估使用肺癌数据概念

Behera et al. BioMedical Engineering OnLine 2011, 10:97 /content/10/1/97 RESEARCH Open Access Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data 1 2 1 3 4 5 Madhusmita Behera , Erin E Fowler , Taofeek K Owonikoko , Walker H Land , William Mayfield , Zhengjia Chen , Fadlo R Khuri1, Suresh S Ramalingam1 and John J Heine2* * Correspondence: john. Abstract heine@ 2Department of Cancer Background: Statistical learning (SL) techniques can address non-linear relationships Epidemiology, H. Lee Moffitt Cancer Center Research Institute, and small datasets but do not provide an output that has an epidemiologic 12902 Magnolia Drive, MRC- interpretation. CANCONT, Tampa, FL 33612, USA Full list of author information is Methods: A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer available at the end of the article patients was used to evaluate an approach for using SL methods as a preprocessing step for survival analysis. A stochastic method of training a probabilistic neural network (PNN) was used with differential evolution (DE) optimization. Survival scores were derived stochastically by combining CVs with the PNN. Patients (n = 151) were dichotomized into favorable (n = 92) and unfavorable (n = 59) survival outcome groups. These PNN derived scores were used with logistic regression (LR) modeling to predict favorable survival outcome and were int