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

Yingchun Zhou1, Rong Huang1, Shanshan Yu1

  • 11 Department of Statistics and Actuarial Sciences, East China Normal University, Shanghai, P.R. China.

Statistical Methods in Medical Research
|January 5, 2018
PubMed
Summary

This study introduces a novel quantile-based method for classifying cardiovascular diseases using electrocardiogram data. The approach effectively reduces data dimensions, aiding in biomarker discovery and improving classification accuracy.

Keywords:
Optimal quantile levelbiomarker identificationdisease classificationelectrocardiogram analysisquantile treatment difference

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Area of Science:

  • Biomedical research
  • Medical informatics
  • Cardiology

Background:

  • Classification and biomarker discovery are crucial in modern medical research, especially with high-dimensional data.
  • Electrocardiogram (ECG) analysis presents challenges due to numerous variables and measurements.

Purpose of the Study:

  • To develop an optimal quantile level selection procedure for dimension reduction in ECG data.
  • To enhance classification of cardiovascular diseases and facilitate biomarker discovery.

Main Methods:

  • A novel quantile-based procedure for optimal quantile level selection was proposed.
  • Dimension reduction was achieved by characterizing data distributions using quantiles.
  • The reduced data was combined with classification tools for analysis.

Main Results:

  • The proposed method demonstrated effective dimension reduction for high-dimensional ECG data.
  • Sensible classification results for cardiovascular diseases were achieved.
  • The method proved effective for biomarker discovery.

Conclusions:

  • The optimal quantile level selection procedure offers a robust approach for ECG data analysis.
  • This method enhances both cardiovascular disease classification and biomarker discovery.
  • The approach is validated through simulation and real-world data analysis.