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Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection

Ioannis D Apostolopoulos1, Peter P Groumpos2, Dimitris J Apostolopoulos3

  • 1University of Patras, Medical School, Department of Medical Physics, Rio, Achaia, PC 26504, Greece.

Biomedical Physics & Engineering Express
|April 30, 2021
PubMed

Insights

A new Advanced Fuzzy Cognitive Maps model accurately diagnoses Coronary Artery Disease (CAD) with 85.47% accuracy. This computer-aided diagnostic tool offers a 7% improvement over traditional methods, aiding in the prevention of cardiovascular diseases.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Cardiovascular Diseases (CVD) cause 50% of deaths in the EU.
  • 80% of premature heart disease and stroke are preventable.
  • Accurate diagnosis of Coronary Artery Disease (CAD) is crucial for effective intervention.

Purpose of the Study:

  • To propose a novel Computer-Aided Diagnostic (CAD) model.
  • To enhance the precision and interpretability of diagnostic decision-making for CAD.

Main Methods:

  • Utilized State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of traditional Fuzzy Cognitive Maps.
  • Incorporated a rule-based mechanism to augment system knowledge and decision interpretability.
  • Evaluated the model on a CAD dataset from the University Hospital of Patras, Greece.

Main Results:

  • The proposed AFCM model achieved 85.47% accuracy in CAD diagnosis.
  • Demonstrated a +7% improvement in accuracy compared to traditional Fuzzy Cognitive Maps (FCM).
  • Experiments confirmed the effectiveness and reliability of the AFCM approach.

Conclusions:

  • The Advanced Fuzzy Cognitive Maps (AFCM) approach significantly outperforms conventional methods for CAD diagnosis.
  • AFCM offers a reliable and interpretable tool for the precise diagnosis of Coronary Artery Disease.
  • This technology holds potential for improving cardiovascular disease management and prevention.

Keywords:
coronary artery diseasedecision support systemmachine learningstate space fuzzy cognitive maps

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