Titlebook: Machine Learning in Medicine - Cookbook Three; Ton J. Cleophas,Aeilko H. Zwinderman Book 2014 The Author(s) 2014 Computer science.Data min

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Training Decision Trees for a More Meaningful Accuracy (150 Patients with Pneumonia)book One, pp. 97–104, Decision trees for decision analysis, Springer Heidelberg Germany, 2014, from the same authors). However, this method is not entirely appropriate, because a decision tree is built from a data file, and, subsequently, the same data file is applied once more for computing the hea

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Loglinear Modeling for Outcome Categories (Quality of Life Scores in 445 Patients with Different Perfe (QOL) (see Machine Learning in Medicine Cookbook Two, Chap. 4, pp. 23–25, Polynomial regression for outcome categories, 2014, Springer Heidelberg Germany, from the same authors). An alternative approach is logit loglinear modeling. The latter method does not use continuous predictors on a case by

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Support Vector Machines for Imperfect Nonlinear Data (200 Patients with Sepsis)ses and controls as good as possible. Discriminant analysis, classification trees, and neural networks (see Machine Learning in Medicine Part One, Chap. 17, Discriminant analysis for supervised data, pp. 215–224, Chap. 13, Artificial Intelligence, Chaps. 12 and 13, pp. 145–165, 2013, and Machine Lea