## 5 Experimental Results and Simulations
### Evaluation of ADASYN Technique  
The collected ECG data is an imbalanced dataset. One of the most popular methods for handling imbalanced data is called SMOTE (Synthetic Minority Over-sampling Technique). SMOTE generates synthetic samples between every positive sample and one of its close sample [44], and Adaptive synthetic sampling (ADASYN), which finds a weighted distribution for many minority classes their difficulty through the learning process. In ADASYN, several synthetic data is created for minority class to assist the learning process and reduce the complexity [45].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Accuracy** & **TPR** & **TNR** & **AUC** & **G-Mean** & **Precision** & **Fscore** \\ \hline DT & 75.04\% & 95.53\% & 30.88\% & 63.21\% & 54.32\% & 74.86\% & 83.94\% \\ LDA & 72.43\% & 92.75\% & 28.68\% & 60.71\% & 51.58\% & 73.69\% & 82.13\% \\ LR & 72.42\% & 92.06\% & 30.11\% & 61.09\% & 52.65\% & 73.94\% & 82.01\% \\ NB & 69.97\% & 96.41\% & 13.04\% & 54.72\% & 35.45\% & 70.48\% & 81.43\% \\ KNN & 75.62\% & 90.72\% & 43.09\% & 66.90\% & 62.52\% & 77.44\% & 83.56\% \\ BT & 75.98\% & 93.64\% & 37.94\% & 65.79\% & 59.60\% & 76.47\% & 84.19\% \\ SVM & 70.24\% & 99.04\% & 8.23\% & 53.63\% & 28.55\% & 69.92\% & 81.97\% \\ DT* & 75.92\% & 94.78\% & 35.31\% & 65.04\% & 57.85\% & 75.94\% & 84.32\% \\ DA* & 72.52\% & 93.03\% & 28.34\% & 60.69\% & 51.35\% & 73.66\% & 82.22\% \\ NB* & 71.00\% & 80.96\% & 49.54\% & 65.25\% & 63.33\% & 77.56\% & 79.22\% \\ KNN* & **76.50\%** & 90.81\% & 45.67\% & **68.24\%** & **64.40\%** & 78.26\% & 84.07\% \\ ensemble DT* & **77.26\%** & 92.95\% & 43.47\% & **68.21\%** & **63.56\%** & 77.98\% & 84.81\% \\ SVM* & 74.82\% & 95.70\% & 29.84\% & 62.77\% & 53.44\% & 74.61\% & 83.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different classification methods [X* denotes the optimized classifier X].  
In this sub-section, we investigate the impact of the ADASYN technique on the learning model's performance. Table 7 shows the KNN* classifier results using different oversampling ratios. It is clear that at the ratio equals 0.5, the performance of KNN* was highly robust with an AUC value equals 70.47. Although the accuracy is decreased compared to other ratios (see Figure 6), however, based on reported results of G-mean and AUC, the obtained model was more robust and had stable performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ratio** & **Accuracy** & **TPR** & **TNR** & **AUC** & **Precision** & **Fscore** & **G-Mean** \\ \hline
0.0 & 76.50\% & 90.81\% & 45.67\% & 68.24\% & 78.26\% & 84.07\% & 64.40\% \\
0.1 & 76.53\% & 90.60\% & 46.23\% & 68.42\% & 78.40\% & 84.06\% & 64.72\% \\
0.2 & 76.53\% & 90.60\% & 46.23\% & 68.42\% & 78.40\% & 84.06\% & 64.72\% \\
0.3 & 75.27\% & 85.21\% & 53.88\% & 69.54\% & 79.92\% & 82.48\% & 67.76\% \\
0.4 & 73.20\% & 77.96\% & 62.94\% & 70.45\% & 81.92\% & 79.89\% & 70.05\% \\
0.5 & 73.21\% & 77.97\% & 62.96\% & **70.47\%** & 81.93\% & 79.90\% & 70.07\% \\
0.6 & 71.92\% & 74.60\% & 66.15\% & 70.37\% & 82.60\% & 78.39\% & **70.25\%** \\
0.7 & 70.41\% & 71.20\% & 68.71\% & 69.96\% & 83.06\% & 76.67\% & 69.95\% \\
0.8 & 68.02\% & 65.68\% & 73.05\% & 69.36\% & 84.00\% & 73.72\% & 69.27\% \\
0.9 & 67.36\% & 63.92\% & 74.76\% & 69.34\% & 84.51\% & 72.79\% & 69.13\% \\
1.0 & 67.30\% & 63.84\% & 74.74\% & 69.29\% & 84.48\% & 72.73\% & 69.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of KNN* classifier using different oversampling ratio.  
Figure 5: Minimum classification error plot for four classifiers.