Patient-centered yes/no prognosis using learning machines

Authors:
I. R. Konig;J. D. Malley;S. Pajevic;C. Weimar;H-C. Diener;A. Ziegler
Affiliations:
Institut fur Medizinische Biometrie und Statistik, Universitat zu Lubeck, Ratzeburger Allee 160, 23538 Lubeck, Germany.;Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.;Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.;Klinik und Poliklinik fur Neurologie, Universitat Duisburg-Essen, Germany.;Klinik und Poliklinik fur Neurologie, Universitat Duisburg-Essen, Germany.;Institut fur Medizinische Biometrie und Statistik, Universitat zu Lubeck, Ratzeburger Allee 160, 23538 Lubeck, Germany
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2008

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Abstract

In the last 15 years several machine learning approaches have been developed for classification and regression. In an intuitive manner we introduce the main ideas of classification and regression trees, support vector machines, bagging, boosting and random forests. We discuss differences in the use of machine learning in the biomedical community and the computer sciences. We propose methods for comparing machines on a sound statistical basis. Data from the German Stroke Study Collaboration is used for illustration. We compare the results from learning machines to those obtained by a published logistic regression and discuss similarities and differences.