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A speedy cardiovascular diseases classifier using multiple criteria decision analysis.

Wah Ching Lee1, Faan Hei Hung2, Kim Fung Tsang3

  • 1Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China. enwclee@polyu.edu.hk.

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Summary

A new cardiovascular disease classifier (CDC) aids speedy auto-diagnosis. This approach uses analytic hierarchy process (AHP) and multiple criteria decision analysis (MCDA) for efficient patient assessment, improving early detection rates.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Cardiovascular diseases (CVDs) cause 30% of global deaths annually.
  • Aging populations and medical staff shortages exacerbate CVDs.
  • Existing cardiovascular disease classifiers (CDCs) lack rapid evaluation capabilities.

Purpose of the Study:

  • To develop a novel cardiovascular disease classifier (CDC) for rapid auto-diagnosis.
  • To address the limitations of previous CDCs in terms of speed.
  • To improve the efficiency of cardiovascular disease detection.

Main Methods:

  • Incorporated analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA).
  • Developed feature vectors using a Support Vector Machine.
  • MCDA was used for efficient patient weighting and feature selection.

Main Results:

  • Successfully implemented a speedy detection of cardiovascular diseases.
  • The new CDC utilizes the most meaningful features for accurate discrimination.
  • Reduced the number of features required for classification through MCDA.

Conclusions:

  • The developed CDC enables rapid and accurate cardiovascular disease diagnosis.
  • This automated approach can help mitigate the impact of medical personnel shortages.
  • The integration of AHP-MCDA offers a promising strategy for improving diagnostic efficiency.