Bayesian Network Model for Cardiovascular Diseases Diagnosis

In the last few arrays, cardiovascular disorders are a major concern for global health. The prevalence of these heart conditions is constantly increasing, posing a significant challenge for healthcare systems worldwide. Considering this reality, our work aims to develop a precise and reliable diagnostic tool that is essential for effectively detecting these disorders. In this paper, we propose a hybrid approach that combines two processing methods. The first method involves symlet-4 wavelet analysis, while the second method utilizes modeling through a naive Bayesian network. The objective of this work is to detect cardiovascular disorders from ECG signals and determine the classes of arrhythmias present, distinguishing them from normal ones. Our method has shown promising results and will contribute to improving the management of patients with cardiovascular disorders while reducing potential complications associated with these serious conditions.