# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 4 Results
### Study Case 3--Oversampling and Undersampling  
In the final scenario proposed, a novel approach was adopted, combining oversampling with undersampling techniques. This study case thoroughly examined various SMOTE methods outlined in the methods section, each integrated with the NearMiss undersampling technique. By analyzing these combined approaches, this study aimed to provide a comprehensive understanding of their effectiveness in mitigating class imbalance. Each oversampling technique associated with the aforementioned NearMiss method was executed ten times. Following each iteration, the above performance metrics were  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric Method** & **SMOTE** & **ROS** \\ \hline TP & 75.1 \(\pm\) 3.107 & 75.2 \(\pm\) 4.442 \\ FN & 17.9 \(\pm\) 3.107 & 17.8 \(\pm\) 4.442 \\ TN & 1396.9 \(\pm\) 14.571 & 1382.1 \(\pm\) 30.468 \\ FP & 55.1 \(\pm\) 14.571 & 69.9 \(\pm\) 30.468 \\ Sensitivity & 0.808 \(\pm\) 0.033 & 0.809 \(\pm\) 0.048 \\ Specificity & 0.962 \(\pm\) 0.010 & 0.952 \(\pm\) 0.021 \\ Bal. Accuracy & **0.885 \(\pm\) 0.017** & **0.880 \(\pm\) 0.027** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representation of all averages and standard deviations obtained after the 10 iterations of each of the oversampling methods, SMOTE and ROS, in the SC1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric Method** & **NearMiss** & **RUS** \\ \hline TP & 62.7 \(\pm\) 3.466 & 58.6 \(\pm\) 4.904 \\ FN & 30.3 \(\pm\) 3.466 & 34.4 \(\pm\) 4.904 \\ TN & 1297.8 \(\pm\) 74.207 & 1314.4 \(\pm\) 91.951 \\ FP & 154.2 \(\pm\) 74.207 & 137.6 \(\pm\) 91.951 \\ Sensitivity & 0.674 \(\pm\) 0.037 & 0.630 \(\pm\) 0.053 \\ Specificity & 0.894 \(\pm\) 0.051 & 0.905 \(\pm\) 0.063 \\ Bal. Accuracy & **0.784 \(\pm\) 0.024** & **0.768 \(\pm\) 0.033** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Representation of all averages and standard deviations obtained after the 10 iterations of each of the undersampling methods, NearMiss and RUS, in the SC2.  
calculated. Finally, the average and standard deviations are presented for each combination. As illustrated in Table 3, the methods exhibiting the most favorable outcomes are consistently the ones with the highest performance metrics (colored in green): DBSMOTE, CCR, and NDO_sampling, which obtained the best outcome balance accuracy of 91.2%. Conversely, at the opposite end of the spectrum, the methods yielding the lowest results consistently manifested shortcomings in performance (colored in red): SMODB and DE_oversampling with 69.3% and 70.8% balance accuracy, respectively.  
The results corresponding to the methods with the best performance present sensitivity and specificity values greater than 0.80 and 0.95, respectively. The best sensitivity score corresponds to NDO_sampling with 0.870 \(\pm\) 0.040. For its part, the best specificity is provided by the G-SMOTE method with 0.976 \(\pm\) 0.007. Although none of the analyzed techniques offers a simultaneous improvement in both measures, the sensitivity decrease experienced by the last one (0.776 \(\pm\) 0.042) is noticeably greater than can be seen for the specificity in NDO_sampling (0.954 \(\pm\) 0.013). Finally, as can be seen, the worse performing algorithms (marked in red in Table 3) present a noticeable reduction for both scores at the same time.