The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data

• Published in: Journal of the American Medical Informatics Association : JAMIA 2011-07, Vol.18 (4), p.370-375
• Main Authors: Wei, Wei, Visweswaran, Shyam, Cooper, Gregory F
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - genetics; Apolipoproteins E - genetics; Artificial Intelligence; Bayes Theorem; Case-Control Studies; Genome-Wide Association Study; Humans; Models, Genetic; Polymorphism, Single Nucleotide; Prognosis; Research and Applications; ROC Curve
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1136/amiajnl-2011-000101

Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models. This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB). Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time. The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p