## 4 Results
### Proposed Model (HRIDM)  
In the final experiment, our proposed HRIDM model was trained for 20 epochs, similar to the other models. As seen in Figure 13, the model showed consistently high accuracy, with both training and validation accuracy settling between 95 and 97%. This demonstrates high learning capacity, as the model accurately caught the patterns in the training data and generalized well to previously encountered data in the validation set. The precision graphs support the model's learning progress. Training precision was roughly 80%, whereas validation precision was around 70% (Figure 13a). These findings indicate the HRIDM model's capacity to achieve excellent accuracy and precision on both training and validation datasets.  
Figure 13b depicts the decreasing trend in both training and validation loss across the training procedure. This represents a satisfactory convergence to the ideal solution (global minimum). Over the course of 20 epochs, the training loss dropped from above 0.15 to less than 0.090. The validation loss followed a similar pattern, beginning over 0.14 and decreasing to less than 0.085. Lower loss values suggest that the model is better able to predict the target variable (abnormality in ECG data). Finally, the AUC values for both training and validation data remained around 70%, with an increasing trend noted near the end of training. This indicates the model's efficacy in terms of accuracy, recall (as represented by AUC), and overall classification ability. These combined findings demonstrate the HRIDM model's effectiveness. The model improved its ability to detect anomalies in the big ECG dataset by including both inception and residual attributes in the model's structure.  
After training the models, we tested them to find their performance on the unseen dataset, shown in Table 2. This section provides a comprehensive analysis of the results obtained from all the implemented models, including the proposed model. As shown  
Figure 13: Training history curve of proposed model on training and validation data. (**a**) Accuracy, precision, and AUC. (**b**) Loss.  
in Figure 14, the proposed model achieved significantly higher accuracy than previous models, achieving a test score of 50.87%. The Inception network emerged as the second-best model, with a test score of 40.6%, outperforming most of the models mentioned in the literature. On the other hand, ResNet-50, VGG-16, and AlexNet demonstrated comparable performance, with test scores ranging from 33.8% to 35.1%. However, LeNet-5 and LSTM did not meet the performance standards. This is likely due to the highly imbalanced class distribution in the data, making it difficult for these models to learn complex patterns.  
To provide a visual representation of the results, Figure 15 presents the confusion matrices. The \(X\)-axis of each matrix represents the actual values, while the \(Y\)-axis represents the predicted values of our model. The obtained challenge score for our model, using the evaluation matrix of the challenge site, was 0.50897. The confusion matrices demonstrate the model's attempt to classify each class label despite the highly imbalanced data. Among SOTA models, our proposed model outperformed others, exhibiting favorable performance compared to previous approaches mentioned in the literature review.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Test Score** \\ \hline LeNet-5 & 15.7\% \\ AlexNet & 33.8\% \\ VGG16 & 34.9\% \\ ResNet50 & 35.1\% \\ Inception & 40.6\% \\ LSTM & 17.9\% \\
**Proposed (HRIDM)** & **50.87\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing accuracy achieved by multiple SOTA models.  
Figure 14: Accuracy comparison of SOTA vs. proposed model on test dataset.