title: Risk estimation and risk prediction using machine-learning methods.
abstract: After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.
authors: Institut für Medizininsche Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Maria-Goeppert-Str. 1, 23562 Lübeck, Germany.
authors: Jochen
authors: J
authors: Kruppa
authors: Andreas
authors: A
authors: Ziegler
authors: Inke R
authors: IR
authors: König
mesh_terms: D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000235:genetics; D001185:Artificial Intelligence; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D011336:Probability; D015203:Reproducibility of Results; D018570:Risk Assessment