## 4 Results and Discussion  
The comparison of three main sequence models, i.e. Vanilla RNN, LSTM, and GRU is used to classify MI and the healthy control. For all sequence model classifiers, the same hyper-parameters are used. Adam optimization method with learning rate as 0.0001 and 100 epochs were trained in Jupyter Notebook on GPU NVIDIA GeForce RTX 2080. The average of each epoch for the most complex classifier was 13 s. In the sequence model classifier, the number of feed-forward neural network (FFNN) in a unit is different, with vanilla RNN, LSTM, and GRU is one, four, and three FFNN, respectively. The number of FFNN is represented by gates in LSTM and GRU. Knowing the number of FFNN before and after quantization of the sequence model will be useful because it can reduce the size of the model file or even reduce the time needed for model inference. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Hidden State Model** \\ \hline RNN & \(h_{t}=\tanh(\sum\limits_{k=1}^{t}W_{c}^{t-k}W_{in}x_{k})\) \\ LSTM & \(h_{t}=\sigma(W_{o}I_{t})\tanh(\sum\limits_{k=1}^{t}\prod\limits_{j=k+1}^{t} \sigma(W_{j}I_{j}))\sigma(W_{i}I_{k})\tanh(W_{in}I_{k}))\) \\ GRU & \(h_{t}=\sum\limits_{k=1}^{t}\left[\prod\limits_{j=k+1}^{t}\sigma(W_{k}I_{j}) \right](1-\sigma(W_{k}x_{k}))\tanh(Wx_{k})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Hidden State in Sequence Modeling Classifier. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Diagnostic** & **MI** & **Healthy Control** & **Total** \\ \hline MI & True Positive (TP) & False Positive (FP) & All Positive Test (T+) \\ Healthy Control & False Negative (FN) & True Negative (TN) & All Negative Test (T-) \\ Total & Total of MI & Total of Healthy Control & Total Sample \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Diagnostic Test. Furthermore, five different data partitioning ratios of the training and testing set are also compared in the sequence modeling classifier. It consists of 90%:10%, 80%:20%, 70%:30%, 60%:40% and 50%:50% for the training and testing set, respectively (as presented in Table 3). A value of 12.359 of the sequential data is randomly separated with automatic data splitting (shuffled sampling). The training set used is not used for testing and vice versa. We trained all the sequence model classifiers to obtain an optimum model. We have a large imbalanced class of the healthy control and MI i.e., a 4.57 imbalanced ratio. We split the training set to be larger than the testing set initially prior to the data partitioning with the same ratio. As overall, the good result in all data partitioning with training larger than testing set is 90%:10% in all the sequence model classifiers with the average sensitivity, specificity, precision and F1 scores being 90.45%, 94.66%, 93.37% and 91.79%, respectively. Due to having a larger training set, the algorithms are better for understanding the patterns in the set and learning to identify specific examples of training sets. This can optimize the computational time of the validation in the long-term because it can prevent too much overfitting. To evaluate the classifier performance in the imbalanced data, Table 3 describes the results of evaluating binary classifications using BACC and MCC. If the comparison of data between two classes is balanced, it is not recommended to use BACC. The'regular' accuracy metric is sufficient. The average proportion corrects each class individually calculated by BACC. Otherwise, MCC takes values in the interval [\(-\)1. 1] with 1 showing a complete agreement and \(-\)1 refer to a complete disagreement and 0 showing that the prediction was uncorrelated with label [26]. A coefficient of +1 represents a perfect prediction due to takes into account the balance ratios of the TP, TN, FP and FN categories. The best result in all data partitioning ratio, the average BACC and MCC is 94.81% and 92.98%, respectively. All data partitioning as presented in Table 3 shows that Vanilla RNN or standard RNN does not learn properly.