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Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
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An Associative Memory Approach to Healthcare Monitoring and Decision Making.

Mario Aldape-Pérez1, Antonio Alarcón-Paredes2, Cornelio Yáñez-Márquez3

  • 1Instituto Politécnico Nacional, Computational Intelligence Laboratory at CIDETEC, Ciudad de Mexico 07700, Mexico. maldape@ipn.mx.

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Summary
This summary is machine-generated.

This study introduces an embedded system using machine learning for biometric signal analysis to detect coronary artery disease. The developed system demonstrates superior accuracy compared to fifty existing algorithms.

Keywords:
Internet of Thingsassociative memoriesdecision support systemse-Healthpattern classification

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Diagnostics

Background:

  • The digital transformation of healthcare, known as e-Health, is driven by increased connectivity and ubiquitous computing.
  • Miniaturization of sensors and communication technology enables advanced health monitoring solutions.

Purpose of the Study:

  • To present an embedded system for real-time monitoring, learning, and classification of biometric signals.
  • To develop a machine learning model using associative memories for predicting coronary artery disease (CAD).

Main Methods:

  • Design and implementation of an embedded system for biometric signal acquisition.
  • Development of a machine learning model employing associative memories for CAD prediction.
  • Comparative analysis against fifty established algorithms for performance evaluation.

Main Results:

  • The embedded system effectively monitors and classifies biometric signals.
  • The associative memory-based machine learning model achieved high classification accuracy, sensitivity, and specificity.
  • The proposed system outperformed fifty widely recognized algorithms in CAD detection.

Conclusions:

  • The developed embedded system offers a promising approach for non-invasive coronary artery disease detection.
  • Machine learning models based on associative memories can significantly enhance diagnostic capabilities in e-Health.
  • This technology has the potential to improve cardiovascular patient care through early and accurate disease prediction.