Patient-centered yes/no prognosis using learning machines. (22nd December 2008)

• Record Type: Journal Article
• Authors: Konig, I.R.; Malley, J.D.; Pajevic, S.; Weimar, C.; Diener, H-C.; Ziegler, A.
• Is Part Of: International journal of data mining and bioinformatics. Volume 2:Number 4(2008)
• Page Start: 289
• Page End: 341
• Publication Date: 2008-12-22
• Subjects: bagging -- boosting -- random forests -- acute ischemic strokes -- support vector machines -- SVM -- machine learning -- data mining -- bioinformatics -- classification -- regression trees -- patient-centred prognosis -- prognostic studies -- biomedical prognosis -- clinical epidemiology -- tutorial -- medical prognosis; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.