In the diagnosis of cardiac disorders Heart sound has a major role, and early detection is crucial to safeguard the patients. Computerized strategies of heart sound classification advocate intensive and more exact results in a quick and better manner. U sing a hybrid optimization-controlled deep learning strategy this paper proposed an automatic heart sound classification module. The parameter tuning of the Deep Neural Network (DNN) classifier in a satisfactory manner is the importance of this research which depends on the Hybrid Sneaky optimization algorithm. The developed sneaky optimization algorithm inherits the traits of questing and societal search agents. Moreover, input data from the Phonocardiogram (PCG) database undergoes the process of feature extraction which extract the important features, like statistical, Heart Rate Variability (HRV), and to enhance the performance of this model, the features of Mel frequency Cepstral coefficients (MFCC) are assisted. The developed Sneaky optimization-based DNN classifier's performance is determined in respect of the metrics, namely precision, accuracy, specificity, and sensitivity, which are around 97%, 96.98%, 97%, and 96.9%, respectively.
Keyphrases
- neural network
- deep learning
- machine learning
- heart rate variability
- heart failure
- artificial intelligence
- convolutional neural network
- end stage renal disease
- heart rate
- atrial fibrillation
- big data
- peritoneal dialysis
- newly diagnosed
- oxidative stress
- gene expression
- blood pressure
- patient reported outcomes
- prognostic factors
- emergency department
- density functional theory
- adverse drug
- patient reported
- anti inflammatory