An Efficient Automatic Detection of Cardiovascular Disease Based on Machine Learning

Abstract

Cardiovascular diseases have become one of the most common threats to human health worldwide. As a non-invasive diagnostic tool, heart sound detection techniques play an important role in predicting cardiovascular diseases. Although the Electrocardiogram (ECG) signal is generally used to diagnose heart disease, due to the low spatial resolution of this signal, the Phonocardiogram (PCG) signal and methods based on sound processing can be used. In this paper, after extracting different features from PCG, patients were classified with the help of algorithms based on artificial intelligence. The simulation results showed that using the eXtreme Gradient Boosting(XGBoost) algorithm has a better performance in detecting cardiovascular patients than other methods. The values of specificity, sensitivity, and accuracy were obtained as 99±1.93%, 98±2.76% and 99±1.78%, respectively. Using the method proposed in this paper can greatly help doctors make accurate and quick diagnoses of cardiovascular patients and be effective in screening patients. In the future, this method can be developed to diagnose heart valve diseases.