Optimal quantile level selection for disease classification and biomarker discovery with application to electrocardiogram data

Yingchun Zhou, Rong Huang, Shanshan Yu, Yanyuan Ma

Research output: Contribution to journalArticle

Abstract

Classification with a large number of predictors and biomarker discovery become increasingly important in biological and medical research. This paper focuses on performing classification of cardiovascular diseases based on electrocardiogram analysis which deals with many variables and a lot of measurements within variables. We propose an optimal quantile level selection procedure to reduce dimension by characterizing distributions with quantiles and combine with classification tools to produce sensible classification and biomarker discovery results. Simulation and an intensive study of a real data set are performed to illustrate the performance of the proposed method.

Original languageEnglish (US)
Pages (from-to)3340-3349
Number of pages10
JournalStatistical Methods in Medical Research
Volume27
Issue number11
DOIs
StatePublished - Nov 1 2018

Fingerprint

Biomarkers
Quantile
Electrocardiography
Selection Procedures
Biomedical Research
Predictors
Cardiovascular Diseases
Electrocardiogram
Simulation

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

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Optimal quantile level selection for disease classification and biomarker discovery with application to electrocardiogram data. / Zhou, Yingchun; Huang, Rong; Yu, Shanshan; Ma, Yanyuan.

In: Statistical Methods in Medical Research, Vol. 27, No. 11, 01.11.2018, p. 3340-3349.

Research output: Contribution to journalArticle

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