## 5 Conclusions  
Based on the experiments performed and in using the various combinations of harmonic and percussive feature sets, harmonic features outperformed percussive features for the balanced prediction of both normal and abnormal labels. It can be concluded that the expert system, implemented using a feed-forward neural network with four hidden layers and one output layer, using the TensorFlow Keras library, has shown promising results for classifying normal and abnormal heart sounds. The hyper-parameters, such as learning rate and batch size, were tuned to obtain the best performance of the model. The use of a sequential model allowed for the creation of multiple hidden layers with a high density of neurons, contributing to the overall performance of the model. Moreover, using the Adamax optimizer, a variant of the Adam optimization algorithm proved to be effective in accelerating the optimization process, improving convergence and maintaining stability during training. Additionally, using ReLU activation functions for \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Model** & **Features** & **Results** \\ \hline [PERSON] et al. [23] & Deep Neural Network & MFCC & Acc: 91.21; Sens: NA; Spec: NA \\ \hline [PERSON] et al. [24] & Feed-forward neural network & Power spectrum feature & Acc: 76.7; Sens: 74.7; Spec: 78.8 \\ \hline [PERSON] et al. [25] & Feed-forward ANN and SVM & Temporal and spectral features & Acc: NA; Sens: 84 to 93; Spec: 91 to 99 \\ \hline [PERSON] et al. [26] & Deep Convolutional Neural Networks & Mel-frequency spectral coefficients & Acc: 91.6 to 95.3; Sens: NA; Spec: NA \\ \hline [PERSON] et al. [27] & DropConnected Neural Networks & MFCC and inter-beat features & Acc: 85.2; Sens: NA; Spec: NA \\ \hline [PERSON] et al. [28] & CNN, SVM & CNN-based Deep feature & Acc: 81.2; Sens: 84.8; Spec: 77.6 \\ \hline [PERSON] et al. [29] & Various ML models & Acc: 86; Sens: 94; Spec: 78 \\ \hline [PERSON] et al. [30] & Feed-forward ANN & Time–frequency domain & Acc: 86.5; Sens: 84.4; Spec: 86.9 \\ \hline [PERSON] et al. [31] & Ensemble of Feed-forward ANNs & Power spectral density, linear predictive coefficient (LPC), natural and Tsallis entropy, MFCCs & Acc: 85.90; Sens: 86.91; Spec: 84.90 \\ \hline [PERSON] et al. [32] & AdaBoost-abstain Classifier, CNN & Time-domain features, frequency-domain features, MFCC & Acc: 86.02; Sens: 94.24; Spec: 77.81 \\ \hline Proposed Work & Feed-forward ANN & Chroma & Acc: 93.40; Sens: 82.40; Spec: 80.60 \\ \hline Proposed Work & Feed-forward ANN & MFCC & Acc: 92.60; Sens: 83.80; Spec: 81.50 \\ \hline Proposed Work & Feed-forward ANN & MFCC and Statistical & Acc: 93.50; Sens: 82.80; Spec: 81.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont._hidden layers, batch normalization layers and binary cross-entropy loss during the training process contributed to the stability, accuracy, and the generalization ability of the model. Even though the combinations of all 82 harmonic features achieved a maximum validation accuracy of 93.80%, considering the overall computational complexity, the PCG classification experiment based on 12 chroma harmonic features can also be considered for a better computation performance, where the accuracy is 93.40%, and the area under the curve (AUC) is 0.975, which is good. This work tested the ANN using the PhysioNet Challenge dataset, which is considered a benchmark dataset. Once the proposed ANN framework has been implemented through an embedded system at the primary health centers in India, the early diagnosis of heart patients will be possible. Additionally, it can reduce healthcare costs by preventing complications and expensive treatments. Conceptualization, A.S., V.A. and M.S. ; methodology, A.S., V.A. and M.S. ; validation, A.S.; formal analysis, A.S., V.A. and M.S. ; investigation, A.S.; resources, A.S.; data curation, A.S., V.A. and M.S. ; writing--original draft preparation, A.S., V.A. and M.S. ; writing--review and editing, A.S., V.A. and M.S.