NARXnet Machine Learning Prediction Cardiovascular Disease Event ECG and PCG
Keywords:
PCG sound, machine learning, ECG signals, NARX, LM, SCG, BR.Abstract
In the modern era, cardiovascular diseases (CVD) have become a major health concern. Trained neural network classifiers are widely used to predict abnormalities related to cardiovascular disease (CVD) by analyzing ECG signals and PCG sounds. It effectively conveys the process of deriving signals from the R interval of the ECG signal and processing and segmenting them simultaneously. Different estimation techniques are used after the chaotic, time, and Frequency domain characteristics are derived. This work focuses on using the Bayesian regularization (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) optimization algorithms to train the NARX network. Nonetheless, utilizing a variety of models, including LR, SLR, DT, SVM, Ensemble, GPR, NN, and others, predicts many features extracted from labeled PCG sound and ECG signals. This study evaluates the trained NARX model's prediction performance concerning the three optimization algorithms utilized during the training phase. It compares various machine learning methods for estimating CVD and evaluates the estimation results based on performance criteria. The NARX-BR artificial neural network detects CVD with an R-squared value of 0.968 and MSE of 0.0738 and the highest accuracy, achieved at 98.2%, is observed for Decision Tree for predication cardiovascular.
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