Non-invasive modelling methodology for the diagnosis of Coronary Artery
  Disease using Fuzzy Cognitive Maps
  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.
