# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 5 Discussion  
### Comparison of Similar Work  
Table 11 highlights the results in previous related studies that used HRV signals as the data source for analysis. The accuracy of models built by previous researchers has exceeded 95%, which indicated that there is a big difference in the cardiac system between the normal and CHF groups. However, the number of features they used mostly exceed 10. In contrast, we applied the SVM classifier and only five features for model A with an accuracy of 97.35%. We used a larger sample size to build our model A to make it more stable than previous studies. Previous work is more inclined to find the difference between the normal and CHF groups. In Table 11, researchers have evaluated the different severity levels of CHF disease, and a large number of features were used for binary and four-class classifications [27; 28].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{5}{c}{**Predicted Classes**} & \multicolumn{5}{c}{**Performance Evaluation**} \\ \cline{2-9}  & & **CHF I** & **CHF II** & **CHF III** & **CHF IV** & **Prec (\%)** & **Sens (\%)** & **Spec (\%)** \\ \hline \multirow{4}{*}{True Classes} & CHF I & 3 & 0 & 1 & 0 & 75.00 & 75.00 & 97.30 \\  & CHF II & 1 & 4 & 2 & 0 & 80.00 & 57.14 & 97.06 \\ \cline{1-1}  & CHF III & 0 & 1 & 15 & 0 & 83.33 & 93.75 & 88.00 \\ \cline{1-1}  & CHF IV & 0 & 0 & 0 & 14 & 100 & 100 & 100 \\ \hline \hline \end{tabular}  
* CHF: congestive heart failure; Prec: precision; Sens: sensitivity; Spec: specificity.  
\end{table}
Table 10: Performance of the k-nearest-neighbor (KNN) (K = 1) classifier for building model B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **NF** & **Best Features** & **Acc (\%)** \\ \hline SVM & 4 & PNN50, Mean, RATIO1, SampEn & 78.05 \\ LDA & 3 & PNN20, PNN50, NPHF & 70.73 \\ DT & 3 & PNN50, FuzzyMEn, SD2 & 82.93 \\ NB & 4 & PNN20, SDNN, \(\Delta\)h, SD1 & 70.73 \\
1-NN & 4 & PNN20, PNN50, TOTPWR, FuzzyMEn & 87.80 \\
3-NN & 4 & PNN50, SDNN, TOTPWR, SD1/SD2 & 73.17 \\
5-NN & 3 & PNN20, RMSSD, PVLF & 78.05 \\ \hline \hline \end{tabular}  
* NF: number of features; Acc: accuracy; SVM: support vector machine; LDA: linear discriminant analysis; DT: decision tree; NB: Bayesian.  
\end{table}
Table 9: The performance parameters of five classifier algorithms for building model B.  
However, we applied the KNN (K = 1) classifier and four features for diagnosing the four severity levels of CHF disease with an accuracy of 87.80%. In 2016, [PERSON] et al. [24] divided the RRITS of each recording in the database into a segment with 2000 intervals and applied the SVM classifier and 22 features for CHF discrimination with an accuracy of 97.60%. In 2018, [PERSON] et al. [42] divided the RRITS of each recording in the database into a segment with 300 intervals and applied the CNN classifier and one feature for CHF discrimination with an accuracy of 81.85%. In these two papers, one subject provided more than one RRITS segments in the database, and the use of ten-fold cross-validation to evaluate the performance of classifier means that the data of the training set and the data of the test set may come from the same subject. That was to say, the evaluation of these two models was intra-subject and not cross-subject validation. Because of large similarities between multiple segments of the same subject, intra-subject validation of evaluating models tended to achieve an over-optimistic accuracy. However, the two models we built were evaluated using more robust cross-subject validation, and a higher accuracy was obtained.