title: Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension.
abstract: In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.
authors.affiliation: Department of Statistics, University of Central Florida, Orlando, FL 32816-2370, USA.
authors.forename: Hsin-Hsiung
authors.initials: HH
authors.lastname: Huang
authors.affiliation: Department of Statistics, University of Central Florida, Orlando, FL 32816-2370, USA.
authors.forename: Tu
authors.initials: T
authors.lastname: Xu
authors.affiliation: Department of Statistics, University of Central Florida, Orlando, FL 32816-2370, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Yang