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Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps
Ioannis D Apostolopoulos1, Peter P Groumpos2
1School of Medicine, University of Patras, Rion, Greece.
Insights
This study introduces a novel Medical Decision Support System (MDSS) for predicting Coronary Artery Disease (CAD) using Fuzzy Cognitive Maps (FCM). The FCM-based MDSS achieves 78.2% accuracy, outperforming existing methods in CAD diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Cardiovascular diseases (CVD) and strokes represent a significant global health and economic challenge.
- Coronary Artery Disease (CAD) is the most prevalent form of CVD, with Coronary Angiography being the current diagnostic standard.
- Coronary Angiography is an invasive procedure, highlighting the need for alternative diagnostic approaches.
Purpose of the Study:
- To develop and illustrate a Medical Decision Support System (MDSS) for predicting Coronary Artery Disease (CAD).
- To leverage Fuzzy Cognitive Maps (FCM) for CAD prediction, utilizing patient clinical data.
- To assess the performance of the proposed FCM-based MDSS against established classification algorithms.
Main Methods:
- Development of an MDSS based on Fuzzy Cognitive Maps (FCM).
- FCMs were employed for their ability to handle ambiguity, uncertainty, and adapt to changing environments.
- The system was designed to diagnose CAD using specific patient clinical condition inputs.
Main Results:
- The proposed FCM-based MDSS achieved an accuracy of 78.2% in predicting CAD.
- This performance surpassed several state-of-the-art classification algorithms.
- The model was validated on a dataset from the University Hospital of Patras.
Conclusions:
- Fuzzy Cognitive Maps offer a promising approach for developing intelligent Medical Decision Support Systems.
- The developed MDSS demonstrates effective CAD prediction capabilities.
- The system shows potential as an alternative or supplementary tool for CAD diagnosis.
Abstract:
Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive approach for detection and treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) using Fuzzy Cognitive Maps (FCM). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Cognitive Maps and is intended to diagnose CAD utilizing specific inputs related to the patient's clinical conditions. We show that the proposed model, when tested on a dataset collected from the Laboratory of Nuclear Medicine of the University Hospital of Patras achieves accuracy of 78.2% outmatching several state-of-the-art classification algorithms.
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