Non-invasive modelling methodology for the diagnosis of Coronary Artery Disease using Fuzzy Cognitive Maps
This work addresses the need for non-invasive diagnostic methods for CAD, a common cardiovascular disease, though it appears incremental as it builds on existing fuzzy logic techniques with adjustments.
The paper tackled the problem of diagnosing Coronary Artery Disease (CAD) by proposing a non-invasive Medical Decision Support System using Fuzzy Cognitive Maps, achieving an accuracy of 78.2% on a dataset of 303 patients, which outperformed several state-of-the-art classification algorithms.
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 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) utilizing Fuzzy Cognitive Maps (FCMs). 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 Logic and Fuzzy Cognitive Maps, with some adjustments to improve the results. The proposed model, tested on a labelled CAD dataset of 303 patients, obtains an accuracy of 78.2% outmatching several state-of-the-art classification algorithms.