# Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series
## 4 Results and Discussion
### Accuracy Analysis for the Classification of Cardiovascular Diseases
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Signal Length**} & \multicolumn{4}{c}{**Overall Accuracy (\%)**} \\ \cline{3-5}  & & **MLP** & **ELM** & **RVFL** \\ \hline \multirow{3}{*}{First scenario: databases with 3663 samples} & 512 & 51.64 & 53.55 & 54.23 \\  & 1024 & 50.27 & 54.51 & 54.10 \\  & 2048 & 49.18 & 54.64 & 53.83 \\ \hline \multirow{3}{*}{Second scenario: databases with 10,494 samples} & 512 & 50.52 & 51.91 & 50.29 \\  & 1024 & 50.05 & 50.57 & 50.00 \\ \cline{1-1}  & 2048 & 50.09 & 50.62 & 49.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall accuracy of MLP, ELM, and RVFL approaches within cardiovascular diseases. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Signal Length**} & \multirow{2}{*}{**Epochs**} & \multicolumn{6}{c}{**Overall Accuracy (\%)**} \\ \cline{3-8}  & & & **CNN–MLP** & **CNN–RVFL3** & **CNN–RVFL4** & **CNN–RVFL5** & **CNN–RVFL6** & **CNN–RVFL7** \\ \hline \multirow{8}{*}{First scenario: databases with 3663 samples} & 512 & 51.64 & 53.55 & 54.23 \\  & 1024 & 50.27 & 54.51 & 54.10 \\  & 2048 & 49.18 & 54.64 & 53.83 \\ \hline \multirow{8}{*}{Second scenario: databases with 10,494 samples} & 512 & 50.52 & 51.91 & 50.29 \\  & 1024 & 50.05 & 50.57 & 50.00 \\ \cline{1-1}  & 2048 & 50.09 & 50.62 & 49.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overall accuracy of CNN for the classification of cardiovascular disease. class are reflected in Table 8. In the case of signals with length 2048 in the first scenario, for class I signals, it can be noted that 0.39% of signals are misclassified as class II. In addition, 0.42% of class II signals are misclassified as class I signals. Further, approximately 2.08% of class III signals are wrongly labeled as class I signals. The confusion matrices with lower performance over signals with lengths 512 and 1024 can be interpreted similarly. Focusing now on the second scenario, similar to the first scenario, the CNN-MLP model achieved better results for the series with length 2048. In this case, 0.70% of the class I signals are wrongly labeled as class II, and 0.84% are wrongly classified as class III. For class II, 0.42% and 0.28% are wrongly labeled as class I and class III, respectively. Furthermore, for class III signals, it can be seen that 3.46% of signals are wrongly labeled as class I. Additionally, 98.14% of the signals of length 2048 are correctly classified. From a clinical viewpoint, patients in class I who are misclassified will attract unnecessary attention since the cardiovascular disease is mild or moderate. However, time series incorrectly labeled in classes II and III may cause more serious consequences, such as delayed treatment. In this experiment, associated with the confusion matrices discussed in the previous paragraph, 0.42% and 2.08% of the time series in classes II and III are wrongly classified as another, respectively. In the second scenario, 0.42% of class II series are misclassified, and 2.08% of class III series or signals are mislabeled. The Acc PPV, Sen, and Spe performance metrics for all confusion matrices can be reviewed in Table 8.  
* Performance of CNN for five-fold cross-validation: Here, the paper details the generalization ability of CNN in Tables 9 and 10 under the five-fold cross-validation criterion. As before, 64 and 32 were the number of epochs considered for the first and second scenario databases, respectively. The classification of cardiovascular disease with this training and testing rule was comparable with the scheme without cross-validation (see Tables 7 and 8). The random initialization of parameters (weights and biases) and the average value of the five runs explain the bounded variation between evaluation metrics. This proves the performance of the proposed CNN method and the importance of deep feature extraction in the final model decision.