چکیده (انگلیسی):

Cardiovascular disease or atherosclerosis is any disease affecting
the cardiovascular system. They include coronary heart disease, raised
blood pressure, cerebrovascular disease, peripheral artery disease,
rheumatic heart disease, congenital heart disease and heart failure. They are
treated by cardiologists, thoracic surgeons, vascular surgeons, neurologists
and interventional radiologists. The diagnosis is an important yet
complicated task that needs to be done accurately and efficiently. The
automation of this system is very much needed to help the physicians to do
better diagnosis and treatment. Computer aided diagnosis systems are
widely discussed as classification problems. The objective is to reduce the
number of false decisions and increase the true ones. In this study, we
evaluate the performance of Bayesian classifier (BN) in predicting the risk
of cardiovascular disease. Bayesian networks are selected as they are able
to produce probability estimates rather than predictions. These estimates
allow predictions to be ranked and their expected costs to be minimized.
The major advantage of BN is the ability to represent and hence understand
knowledge. The cardiovascular dataset is provided by University of California,
Irvine (UCI) machine learning repository. It consists of 303 instances of heart
disease data each having 76 variables including the predicted class one. This
study evaluates two Bayesian network classifiers; Tree Augmented Naïve
Bayes and the Markov Blanket Estimation and their prediction accuracies are
benchmarked against the Support Vector Machine. The experimental results
show that Bayesian networks with Markov blanket estimation has a superior
performance on the diagnosis of cardiovascular diseases with classification
accuracy of MBE model is 97.92% of test samples, while TAN and SVM
models have 88.54 and 70.83% respectively.