Classifier identification using deep learning and machine learning algorithms for the detection of valvular heart diseases

Abstract

Heart-related disorders are rapidly growing throughout the world. Artificial Intelligence with computational methods plays a significant role in early detection and diagnosis. This study has been devoted to finding the best classifiers for different valvular heart problems using popular CNN-based deep learning models and machine learning algorithms written in Python 3.8. In this research, the CNN-based Xception network model for the first time has been proposed for valvular heart sound analysis, which achieved an accuracy of 99.45% on the test dataset with a sensitivity of 98.5% and specificity of 98.7%. Compared with other deep learning models like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, and Residual Net, it is observed that accuracy for predicting the prediction of valvular heart disease is the highest, and testing time is the lowest in the proposed modified Xception network model. The features are Root Mean Square, Energy, Power, Zero Crossing Rate, Total Harmonic Distortion, Skewness, and Kurtosis in the time domain. The analysis has been made on heart sounds of normal and diseased patients available from the standard heart sound data repository. Finally, all the evaluated results were compared, and found SVM and Random Forest algorithms are the most effective among machine learning methods. The proposed modified CNN-based Xception model works the best among all deep learning methods.