# Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series
## 4 Results and Discussion
### Accuracy Analysis for the Classification of Cardiovascular Diseases
In conclusion, the current study tackles a classification problem for cardiovascular diseases using neural networks. The methods with vector inputs--MLP, ELM, and RVFL--did not satisfactorily classify the three classes of congestive heart failure; ELM and RVFL--achieved a higher accuracy rate of 54%. Adapting tensor inputs in a CNN structure with eight convolutional layers significantly improved the learning of features for medical diagnosis. The accuracy of the model was evaluated according to the depth of the feature map, coupled separately with the RVFL network for the final classification. The analysis showed that the CNN framework successfully classified the three classes of cardiovascular disease with an accuracy of 98% and 99%. As expected, the classification of the model is \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Signal**} & \multicolumn{5}{c}{**Class I (\%)**} & \multicolumn{5}{c}{**Class II (\%)**} & \multicolumn{5}{c}{**Class III (\%)**} \\ \cline{3-14}  & & **Acc** & **PPV** & **Sen** & **Spec** & **Acc** & **PPV** & **Sen** & **Spec** & **Acc** & **PPV** & **Sen** & **Spec** \\ \hline First scenario: & 512 & 84.22 & 79.49 & 71.86 & 90.51 & 87.63 & 78.82 & 86.10 & 88.40 & 92.06 & 88.22 & 88.40 & 94.12 \\ databases with & 1024 & 97.78 & 96.65 & 96.91 & 98.23 & 99.11 & 99.13 & 98.13 & 99.59 & 98.12 & 96.79 & 97.51 & 98.43 \\
3663 samples & 2048 & 98.16 & 96.71 & 98.09 & 98.23 & 99.86 & 98.75 & 99.59 & 99.21 & 98.29 & 97.85 & 96.82 & 99.00 \\ \hline Second scenario: & 512 & 96.74 & 96.43 & 93.74 & 98.20 & 98.70 & 97.90 & 98.19 & 98.96 & 97.32 & 95.06 & 97.15 & 97.40 \\ databases with & 1024 & 97.32 & 96.61 & 95.31 & 98.33 & 98.89 & 97.91 & 98.80 & 98.85 & 97.99 & 96.94 & 97.21 & 98.38 \\
10,494 samples & 2048 & 97.00 & 96.00 & 98.07 & 97.94 & 99.56 & 99.69 & 98.99 & 99.84 & 98.26 & 98.12 & 96.64 & 99.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation metrics of CCN-MLP for five-fold cross-validation. Figure 4: Confusion matrices of three classes for cardiovascular diseases within the second scenario and performance of CNN–MLP. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Signal**} & \multicolumn{5}{c}{**Overall Accuracy (\%)**} \\ \cline{3-10}  & & **CNN–MLP** & **CNN–RVFL3** & **CNN–RVFL4** & **CNN–RVFL5** & **CNN–RVFL6** & **CNN–RVFL7** \\ \hline First scenario: & 512 & 81.96 & 63.09 & 69.53 & 78.52 & 82.56 & 83.01 \\ databases with & 1024 & 97.51 & 64.01 & 72.87 & 86.21 & 96.10 & 97.58 \\
3663 samples & 2048 & 96.00 & 65.66 & 74.01 & 85.68 & 95.10 & 96.58 \\ \hline Second scenario: & 512 & 96.38 & 65.44 & 76.45 & 86.09 & 96.78 & 97.00 \\ databases with & 1024 & 97.10 & 71.73 & 83.27 & 90.00 & 96.72 & 97.60 \\
10,494 samples & 2048 & 97.90 & 72.87 & 84.04 & 89.69 & 98.31 & 98.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Overall accuracy of CNN for five-fold cross-validation. more robust in terms of the length of the short time series. The CNN model repeated a similar behavior with the cross-validation scheme. The extraction of deep abstract features through convolutional filters is a possible explanation for the better performance of the proposed CNN approach. Without human intervention, the learning system captures important information from the input time series and forms a meaningful representation of the data by combining low-level features. The system could be installed in a low-cost medical electronic device and serve as a preliminary diagnostic tool in health centers where access to a cardiologist is difficult. The results could be sent to a cardiology expert in a clinic or hospital through the internet to minimize the diagnostic time and reduce the number of misdiagnoses.