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Current evidence supports a clear association between clinical and pathologic factors and recurrence-free survival (RFS) in breast cancer patients. The Cox regression model is the most common tool for investigating simultaneously the influence of several factors on the survival time of patients. But it gives no estimate of the degree of separation of the(More)
In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron(More)
Hypertension is a leading cause of heart disease and stroke. In this study, performance of classification techniques is compared in order to predict the risk of essential hypertension disease. A retrospective analysis was performed in 694 subjects (452 patients and 242 controls). We compared performances of three decision trees, four statistical algorithms,(More)
7 8 Abstract 9 In this study, we compared classical principal components analysis (PCA), generalized principal components analysis (GPCA), linear 10 principal components analysis using neural networks (PCA-NN), and non-linear principal components analysis using neural networks 11 (NLPCA-NN). Data were extracted from the patient satisfaction query with(More)
Keywords: Decision tree C&RT CHAID QUEST ID3 C4.5 C5.0 Cox regression Kaplan–Meier Breast cancer Disease-free survival Random survival forests a b s t r a c t The purpose of this study is to determine new prognostic indexes for the differentiation of subgroups of breast cancer patients with the techniques of decision tree algorithms (C&RT, CHAID, QUEST,(More)
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