# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 5 Discussion
The highest recorded specificity value reached 97%. These data translate to an average specificity of approximately 95%, indicating a low rate of false negatives. In simpler terms, the model demonstrates a high accuracy rate in excluding individuals who do not have the syndrome. It is noteworthy that the sensitivity values are comparatively lower than the specificity values. The average sensitivity across iterations stands at 87%, with the highest achieving 94% and the lowest at 81%. Consequently, the model occasionally fails to detect true positives with the same level of accuracy as it identifies true negatives, as indicated by its specificity. In essence, while the test demonstrates proficiency in identifying individuals who truly have the disease, its sensitivity falls short of its specificity. The CCR method demonstrates a performance profile similar to its predecessor. Remarkably, specificity values consistently exceed sensitivity values throughout the range of measurements. Specificity values range from 98% to 91%, while sensitivity values range from 87% to 72%. This pattern suggests a stronger proficiency in identifying true negatives compared to true positives across various evaluation metrics. An examination of the data from SC1 and the results obtained in SC3 reveals minimal discrepancies between the best-performing methods. The most successful instance of SC1, utilizing SMOTE, achieved an accuracy of 88.5%, while the best case of SC3, employing NDO_Sampling, yielded an accuracy of 91.2%. This translates to a difference of less than 3%. Furthermore, the performance of the SMOTE method (88.5%) surpasses the average accuracy (84.5%) achieved by the combined set of SMOTE variants and the NearMiss undersampling method. It is also comparable to the performance of most individual SMOTE variants (86.0%). An evaluation of the sixteen variants explored within the scope of this investigation yielded only three that surpassed the performance achieved by the SMOTE technique employed in SC1. This finding necessitates a thorough examination of the cost-benefit analysis associated with these variants. It is crucial to determine whether the potential benefits garnered from these more intricate approaches outweigh the additional computational resources, methodological complexity, and time investment required for their implementation, compared to the well-established and computationally efficient SMOTE technique. The current focus on this research work prioritizes the robustness and resilience of employed methodologies and metrics. This emphasis ensures the reliability of these methods even in nonideal circumstances, potentially at the expense of achieving the absolute highest  
Figure 3: SC3: Outcomes of the sensitivity, specificity, and balance accuracy of the 16 SMOTE variants run in conjunction with its NearMiss. performance metrics. While sacrificing peak performance, true positive rate, may seem counterintuitive, it fosters a more sustainable and trustworthy approach. This trade-off between peak performance and robustness is a critical consideration when selecting and evaluating oversampling and undersampling methodologies. Henceforth, a custom metric is employed, which prioritizes the classification accuracy of the target class (SMS) over the high classification rates achieved by the neural network. Despite the promising results, this study has some limitations. The relatively small sample size and its nature may have influenced the outcomes. Additionally, while the synthetic data model used was suitable for analyzing this data, it could be improved to capture all possible relationships between the studied variables. Although the number of speakers with SMS is small, they represent a very high percentage of all subjects diagnosed with this disease in Spain at the time of the recordings. In fact, this number constitutes approximately 20% of the total number of people with SMS in the Spanish Smith-Magenis Syndrome Association (ASME).