## 5 Results and Discussion  
In our experiments we classified our extracted features from heart sound signal database (including normal and abnormal category) using three different classifiers, SVM, DNN and centroid based KNN. Table 2 shows the baseline results obtained using MFCCs features and their corresponding accuracies. In order to improve the accuracy, we found out some optimum values for feature extraction such as sampling frequency and feature vector length on which the resulting accuracies were higher than before and the results are shown in Table 3. For better accuracy, new features (DWT) were taken into consideration for our experiments, after experimenting and training our data with DWT we have achieved results shown in Table 4. We can see from Tables 3 and 4 that there is improvement in the accuracy but we considered to combine those features together and train using SVM and DNN, after performing experiments and finding some optimal values for sampling frequency, we arrived at the conclusion that we have much better output than using those features separately, and we can see the results from Table 5. Unfortunately DNN couldn't perform well as compared to SVM and the reason for that is we have very limited amount of data available and we know that DNN requires thousands of files and features for training and classification, so in case if sufficient amount of data were available, DNN could have performed higher than SVM. The highest accuracy we achieved from those experiments was from fused (combined) features we classified; these combined features have never been used for classification of heart sound signal before. Tables 3-6 summarizes the output, and classification in terms of accuracy averaged f1 score sensitivity and specificity of our experiment in detail. Several experiments were performed with MFCCs and DWT with variant frequencies, variant Cepstral coefficient and their corresponding changing accuracy was recorded. From our experiments Tables 3-5 summarize the last and final results of MFCCs, DWT and combined features (MFCCs and DWT) respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Features** & **Accuracy** \\ \hline SVM & MFCC & 87.2\% \\ DNN & MFCC & 82.3\% \\ Centroid displacement base KkNN & MFCC & 72.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Baseline results with MFCCs features. Figure 6: **(a)** Confusion matrix for MFCCs Features and Centroid displacement based KNN classifier; **(b)** Confusion matrix for DWT Features and Centroid displacement based KNN classifier; **(c)** Confusion matrix for MFCCs plus DWT Features and Centroid displacement based KNN classifier; **(d)** Confusion matrix for MFCCs Features and DNN classifier; **(e)** Confusion matrix for DWT Features and DNN classifier; **(f)** Confusion matrix for MFCCs plus DWT Features and DNN classifier; **(g)** Confusion matrix for MFCCs Features and SVM classifier; **(h)** Confusion matrix for DWT Features and SVM classifier; **(i)** Confusion matrix for MFCCs plus DWT Features and SVM classifier. Depending on the size of dataset and type of features in the classification experiment different results can be achieved with variant accuracies. Using fivefold cross validation the evaluation results are achieved as shown in Table 5. It is observed that the overall maximum accuracy of 97.9% (F1 score of 99.7%) has been achieved using combine features of discrete wavelet transform and MFCCs, and training them via SVM classifier. In case of centroid displacement based KNN the highest accuracy achieved is 97.4% (F1 score of 99.2%). Similarly, in case of DNN, the maximum accuracy of 89.3% has been achieved when we used MFCCs Features combined with DWT features, keeping the parameters for all the features same as mentioned in above paragraph, we trained our deep neural network with features (MFCCs and DWT), in deep neural network we kept batch size of 100, three hidden layers and one output layer and 2000 epochs.