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.

Publication Date

6-17-2014

Publication Title

BMC Proceedings

Volume

8

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1186/1753-6561-8-S1-S96

Socpus ID

85018193534 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85018193534

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