A framework for fuzzy expert system creation--application to cardiovascular diseases.

Area of Science:

• Artificial Intelligence
• Biomedical Engineering
• Cardiology

Background:

• Automated diagnosis of cardiovascular diseases is crucial.
• Existing "black box" models lack interpretability.
• Fuzzy expert systems offer a potential solution for interpretable medical diagnostics.

Purpose of the Study:

• To present a methodology for the automated development of interpretable fuzzy expert systems.
• To apply this methodology to cardiovascular disease classification tasks.
• To demonstrate the performance improvement of fuzzy models over crisp models.

Main Methods:

• Developing fuzzy expert systems from crisp models using automated rule transformation.
• Employing stochastic global optimization for parameter adjustment.
• Utilizing expert-defined rules and established cardiovascular databases (MIT-BIH, European ST-T) for training and validation.

Main Results:

• The automated fuzzy expert system methodology successfully classified arrhythmic and ischemic beats.
• Performance significantly escalated from initial crisp models to sophisticated fuzzy models.
• The developed fuzzy models demonstrated superior performance compared to baseline crisp models.

Conclusions:

• The proposed framework offers significant scientific value for automated fuzzy expert system development.
• Fuzzy expert systems provide an interpretable alternative to "black box" approaches in medical diagnostics.
• This methodology enhances the accuracy and interpretability of cardiovascular disease classification.