# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 5 Discussion  
The convenience and non-invasiveness of KS acquisition formed the foundation of our work. On this basis, we employed a DL network to conduct HF prediagnosis research with KS as the research object, which is a significant effort in the field of KS research. Four feature sets were used to study the application of KS signal in HF prediagnosis in detail, including the ST-CWT dataset, ST-MFCC dataset, UST-CWT dataset, and UST-MFCC dataset. DL architectures AlexNet, VGG19, ResNet50, and Xception were used to classify these four datasets. A fivefold cross-validation was used for each model validation, and the performance of each DL classifier was evaluated using Acc, Se, Sp, and AUC. The effects of KS signal segmentation and different feature extraction methods on HF prediagnosis were investigated. According to our research, the recognition effect of neural networks on MFCC features is better than that of CWT features in the KS-based HF prediagnosis, as shown in Tables 2 and 3. When the ST-CWT dataset was used, Xception achieved the highest AUC and ACC of all classifiers (0.979 and 93.4%), followed by VGG19, which had an AUC of 0.965, and Acc of 89.0%. Using the ST-MFCC dataset, Xception achieved an AUC of 0.988, and an Acc of 95.0%. ResNet50 got an AUC of 0.983 and an Acc of 95.0%. Simultaneously, VGG19 and AlexNet performed significantly better in classification in the ST-MFCC dataset than that in the ST-CWT dataset. When the unsegmented signal processing method was used, the neural network classifier demonstrated the same trend in the CWT and MFCC datasets, as shown in Tables 4 and 5. In the UST-MFCC dataset, Xception had an AUC of 0.989, and an Acc of 95.09%, while ResNet50 obtained an AUC of 0.988 and an Acc of 95.4%. In the UST-CWT dataset, Xception's AUC and ACC were reduced to 0.979, and 94.3%, respectively; the AUC and ACC of ResNet50 were reduced to 0.974 and 92.2%, respectively. The average Acc comparison of the classifiers on the four datasets is shown in Figure 9. As we can see, the classification performance of the four classifiers in the unsegmented CWT and the MFCC datasets is better than that obtained by the segmentation method. Therefore, it is reasonable to conclude that although the silent period characteristics of KS signals are very weak, they are significant components of KS, and this component has a non-negligible effect in HF prediagnosis tasks. Based on the classification accuracy of the four classifiers across all data sets, we find that Xception performs exceptionally well, and its overall performance outperforms the other three classifiers. The ResNet50 network, which exhibits outstanding classification performance on these four data sets, is likewise regarded as a potent classifier in KS-based HF pre-diagnosis. Combining the ROC curve comparison results in Figure 8, we believe that Xception and ResNet50 are two excellent deep neural network algorithms in the KS-based HF prediagnosis task. Furthermore, we believe that, in addition to the AUC and ACC, the computational scale and Tc are important parameters for evaluating the model's performance. According to the comparison of the four models (Tables 2-5), while Xception has the highest AUC and ACC, it consumes significantly more computing time than the other three models, which is obviously not conducive to the model's deployment and promotion in portable devices. As a result, we think that ResNet50 may be a better choice in terms of practical application. Scholars have been widely concerned about the early diagnosis of HF, and many researchers have made outstanding contributions to the early diagnosis of HF. These research findings not only served as a useful reference for our work but also provided us with a great deal of inspiration and motivation. As shown in Table 6, we chose a typical acoustic-based HF prediagnosis algorithm and compared their results to our algorithm.