# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## 4 Discussion
Research conducted on other topics, such as the classification of Chagas disease at CinC 2025 (more information about the 2025 CinC challenge, including an overview of the problem, the datasets used and the evaluation metric used, can be obtained at the challenge website, as at the moment it is still underway: [[https://moody-challenge.physionet.org/2025/](https://moody-challenge.physionet.org/2025/)]([https://moody-challenge.physionet.org/2025/](https://moody-challenge.physionet.org/2025/)), accessed on 6 July 2025), also showed that while N-BEATS achieved better results than LSTM in the simpler case presented in the preliminary phase of the competition, but after the problem was complicated by the challenge organisers in the official phase (by removing metadata that allowed the network to associate strongly- and weakly-labeled datasets with expected classification outcome), its results dropped significantly--in preliminary phase N-BEATS achieved the score of 0.605 (being in top 10% of solutions) and LSTM 0.294; in the official phase, after the changes, the same networks obtained scores of 0.068 and 0.195. Recurrent and convolutional networks emerged as the better solutions. Similarly, in the case of the discussed problem, N-BEATS performed better for smaller input sets when a two-lead input was used instead of the full 12-lead input [18]. Scenario E showed that the presented architecture for the problem did not have excessive tendencies to overfit, and there is no need to provide complicated pruning mechanisms. In the experiments, it turned out that adding only a dropout layer or a dropout layer and a one-time prune with 20% L1 regularization is an entirely sufficient solution, while for larger networks, 5% L1 pruning each epoch obtained the best results, as shown in Scenario G.  
As the experiments from scenarios A and F have shown, providing the model with additional information on subsequent input branches, even such information that alone causes worse classification results, improves the overall performance of the classifier. What is interesting is that a comparative experiment conducted with a convolutional network (detailed in Appendix D) shows that the convolutional network does not achieve significant improvement from this architecture in the tested scenario. The multi-branch obtained a slightly lower F1-measure and average challenge score, and a slightly higher maximum challenge score and AUROC. The best result for CNN (as can be seen in Table 11) was obtained from the \(\alpha\) branch containing the raw ECG signal, with inputs with wavelet transformation, PCA, domain knowledge, and both drift removal techniques obtaining worse results. It can be assumed that the convolutional network's ability to automatically optimize filters proved in this scenario to work better than commonly used preprocessing techniques that were needed for the core networks. As the maximum challenge score obtained in the experiment was the highest for the multi-branch architecture (0.567 to 0.562 of the \(\alpha\) branch), there is a possibility that with different hyper-parameters and network size, some improvement can be gained. Further time-consuming testing is required in this case, but we do not expect to achieve such improvement as with the core networks. The ablation study conducted in scenario F showed the impact of excluding individual branches. In no case did removing branches, even the least useful ones in scenario A, such as \(\gamma\), improve the classification result, but only slightly worsened it. Removing the \(\gamma\) branch was also the only case where a significant time gain was observed for the classification of a single time window (from 0.40 ms to 0.24 ms). In cases where classification speed is important, it is possible to consider removing this branch. Otherwise, it can be assumed that the proposed architecture works best with complete available information.