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Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using

Ting Shu1, Bob Zhang1, Yuan Yan Tang1

  • 1Department of Computer and Information Science, University of Macau, Taipa, Macau.

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This study introduces a noninvasive method using facial images to detect heart disease. The novel approach analyzes facial color features, achieving high accuracy and offering a faster, less invasive alternative to traditional diagnostics.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Traditional Chinese Medicine

Background:

  • Heart disease remains the leading global cause of mortality.
  • Current diagnostic methods (e.g., blood tests, ECG, CT, MRI) are often time-consuming and invasive.
  • There is a need for efficient, noninvasive heart disease detection techniques.

Purpose of the Study:

  • To propose and validate a noninvasive computerized method for quantitative heart disease detection using facial images.
  • To explore the application of Traditional Chinese Medicine principles in facial analysis for disease detection.
  • To optimize and evaluate a specific machine learning classifier for this task.

Main Methods:

  • Extraction of facial key block color features from digital images.
  • Utilizing the Probabilistic Collaborative Representation Based Classifier (PCRBC) for analysis.
  • Parameter optimization of the PCRBC was performed.
  • Experimentation on a dataset of 581 heart disease and 581 healthy individuals.

Main Results:

  • The proposed facial image analysis method achieved the highest accuracy compared to other classifiers.
  • The PCRBC demonstrated effectiveness in quantitative heart disease detection.
  • Facial key block color features derived from Traditional Chinese Medicine principles were found to be informative.

Conclusions:

  • The developed noninvasive method based on facial image analysis is effective for heart disease detection.
  • This approach offers a promising, less invasive, and potentially faster alternative to traditional diagnostic tools.
  • Further research can explore broader applications of facial analysis in cardiovascular health assessment.