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Published on: August 9, 2024
A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system
Hamid Reza Marateb1, Sobhan Goudarzi1
1Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Insights
A new computer-based system effectively screens for coronary artery disease (CAD) using noninvasive methods. This neuro-fuzzy classifier (NFC) shows high accuracy, offering a promising alternative to invasive procedures for CAD diagnosis.
Area of Science:
- Cardiology
- Computer Science
- Medical Informatics
Background:
- Coronary artery disease (CAD) is a leading cause of death and disability globally.
- Current diagnostic methods like invasive coronary angiography are expensive and carry risks.
- There is a need for noninvasive, cost-effective CAD detection tools.
Purpose of the Study:
- To design a computer-based, noninvasive system for diagnosing coronary artery disease (CAD).
- To develop a system that provides clinically interpretable rules for CAD diagnosis.
Main Methods:
- Utilized the Cleveland CAD dataset for analysis.
- Employed a neuro-fuzzy classifier (NFC) with a scaled conjugate gradient algorithm.
- Applied feature selection methods (MLR, sequential FS) to reduce attributes and enhance classifier performance.
Main Results:
- The MLR + NFC model achieved 84% accuracy, 79% sensitivity, and 89% specificity.
- Key predictive features identified include age, ST/heart rate slope, exercise-induced angina, fluoroscopy, and thallium-201 stress scintigraphy.
- The system demonstrated substantial agreement with the gold standard diagnosis.
Conclusions:
- The developed computer-based system is a promising tool for screening coronary artery disease (CAD) patients.
- The noninvasive approach offers a potentially safer and more accessible method for CAD assessment.
- The system's interpretability aids clinical decision-making in CAD diagnosis.
Background:
Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules.
Materials And Methods:
In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier.
Results:
In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results.
Conclusion:
The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.
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