13. Support Vector Machines for Imperfect Nonlinear Data (200 Patients with Sepsis)

The basic aim of support vector machines is to construct the best fit separation line (or with three dimensional data separation plane), separating cases 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 Learning in Medicine Part Three, Chap. 14, Decision Trees, pp. 137–150, 2013, Springer Heidelberg Germany, by the same authors as the current chapter) are alternative methods for the purpose, but support vector machines are generally more stable and sensitive, although heuristic studies to indicate when they perform better are missing. Support vector machines are also often used in automatic modeling that computes the ensembled results of several best fit models (see Machine Learning in Medicine Cookbook Two, Chaps. 18 and 19, Automatic modeling of drug efficacy prediction, and Automatic modeling for clinical event prediction, pp. 99–111, 2014, Springer Heidelberg Germany, from the same authors). This chapter uses the Konstanz Information Miner, a free data mining software package developed at the University of Konstanz, and also used in the Chaps. 1 and 2.