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Review
. 2012 Oct;131(10):1639-54.
doi: 10.1007/s00439-012-1194-y. Epub 2012 Jul 3.

Risk estimation and risk prediction using machine-learning methods

Jochen Kruppa  1 , Andreas Ziegler, Inke R König
Affiliations
Review

Risk estimation and risk prediction using machine-learning methods

Jochen Kruppa et al. Hum Genet. 2012 Oct.

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.

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Figures

Fig. 1
Fig. 1
Path to construct, evaluate and validate a rule of classification or probability estimation
Fig. 2
Fig. 2
Flowchart of the systematic literature search
Fig. 3
Fig. 3
a ROC curves for all methods in selected SNP sets in the test data. b ROC curves for Random Jungle in regression mode in all SNP sets in the test data
Fig. 4
Fig. 4
Brier scores for scores based on lasso or Random Jungle regression in the test data
See this image and copyright information in PMC

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