# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 4 Results  
We obtained four datasets from the preprocessing and feature analysis of the KS signal, which are the segmented-based CWT (ST-CWT) data set, the segmented-based MFCC (ST-MFCC) data set, the unsegmented-based CWT (UST-CWT) data set, and the unsegmented-based MFCC (UST-MFCC) data set. After data augmentation, each dataset contained 996 healthy KS images and 696 patient KS images. The pre-trained AlexNet, VGG19, ResNet50, and Xception networks are used for HF prediagnosis studies based on the four data sets. Model validation was accomplished using a fivefold cross-validation method, and model performance was measured using average Acc, Se, Sp, and Tc. The model's optimization function is SGDM in the calculation; because performance across model structures is inconsistent, the learning rate, bachSize setting, and max epoch were re-optimized and matched. Following our optimization, the parameters of each neural network were as follows:  
**AlexNet:** learning rate 2 \(\times\) 10\({}^{-4}\), bach size 128, and maximum epoch 20. **VGG19:** learning rate 1 \(\times\) 10\({}^{-4}\), bach size 32, and maximum epoch 15. **ResNet50:** learning rate 4 \(\times\) 10\({}^{-3}\), bachSize 64, and maximum epoch 10. **Xception:** learning rate 5 \(\times\) 10\({}^{-4}\), bach size 32, and maximum epoch 15. The computing resources are small workstations equipped with an Intel XEON Platinum 8260L CPU and an NIVIDIA RTX4000 graphics card. Table 2 shows a comparison of classification results for each neural network model on the ST-CWT and ST-MFCC datasets. It can be seen that Xception had the best performance in the ST-CWT datasets, with AUC, Acc, Se, and Sp of 0.979, 93.4%, 94.3%, and 92.2%, respectively. It was followed by VGG19, with AUC (0.965), Acc (89.0%), Se (91.5%), and Sp (85.5%). The ResNet50 came third, and its AUC, Acc, Se, and Sp were 0.962, 89.8%, 91.1%, and 87.9%, respectively. AxleNet came last, with AUC (0.947), Acc (87.0%), Se (91.8%), and Sp (80.2%). AlexNET, VGG19, ResNet50, and Xception had Tcs of 3 min, 9 min, 6 min, and 55 min, respectively. As shown in Table 3, when using the ST-MFCC datasets for HF prediagnosis, Xception performed the best, with AUC (0.988), Acc (95.0%), Se (98.6%), and Sp (89.9%). It was followed by Resnet50, with AUC (0.977), Acc (95.0%), Se (91.1%), and Sp (97.7%). The VGG19 achieved AUC (0.976), Acc (93.0%), Se (94.8%), and Sp (90.5%), and AlexNet obtained AUC (0.951), Acc (90.4%), Se (94.4%), and Sp (84.8%). The average Tc for AlexNET, VGG19, ResNet50, and Xception was 3 min, 9 min, 6 min, and 55 min, respectively. The ROC curve, which is based on the confusion matrix, is not only a comprehensive index that reflects the continuous variables of sensitivity and specificity, but also an important reference for evaluating the prediction ability of the binary classification model. Figure 7(a) shows the receiver operating characteristic (ROC) curves of the four neural \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classifier** & **Acc (\%)** & **Se (\%)** & **Sp (\%)** & **AUC** & **Tc (min)** \\ \hline AlexNet & 87.0 & 91.8 & 80.2 & 0.947 & 3 \\ VGG19 & 89.0 & 91.5 & 85.5 & 0.965 & 9 \\ ResNet50 & 89.8 & 91.1 & 87.9 & 0.962 & 6 \\ Xception & 93.4 & 94.3 & 92.2 & 0.979 & 55 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of four types of neural networks in the ST-CWT datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classifier** & **Acc (\%)** & **Se (\%)** & **Sp (\%)** & **AUC** & **Tc (min)** \\ \hline AlexNet & 90.4 & 94.4 & 84.8 & 0.951 & 3 \\ VGG19 & 93.0 & 94.8 & 90.5 & 0.976 & 9 \\ ResNet50 & 95.0 & 91.1 & 97.7 & 0.983 & 6 \\ Xception & 95.0 & 98.6 & 89.9 & 0.988 & 55 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance of four types of neural networks in the ST-MFCC datasets. networks when classifying HF using the ST-CWT and ST-MFCC data sets. When using the ST-CWT dataset, the four classifiers ranked based on the ROC curve are Xception, VGG19, ResNet50, and AlexNet, with AUC values of 0.979, 0.965, 0.962, and 0.947, respectively.