# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 5 Discussion
Nevertheless, RUS employs a random selection process, which removes instances from the normative class until a predetermined level of balance between classes is achieved. This method does not take into account the proximity of instances to the decision boundary. An interesting observation emerges when comparing the performance of the NearMiss and RUS methods on SMS classification. The NearMiss method demonstrates a clear strength in identifying actual SMS individuals. It successfully classifies 62 out of 93 SMS samples (\(\approx\)67%), exceeding the 58 correctly identified by the RUS method. However, this advantage comes with a trade-off. The NearMiss method exhibits a slight weakness in correctly classifying normal, non-SMS samples. It identifies 1297 normotypic samples as such, while the RUS method achieves a slightly higher accuracy in this category, correctly classifying 1314 normotypic samples. This comparison highlights the importance of considering both aspects of classification performance--the ability to identify true SMS samples and to avoid misclassifying normal samples--when evaluating these methods. The optimal choice between NearMiss and RUS hinges on the specific characteristics of the dataset. If preserving information within the majority class is crucial, as is the case in this dataset, NearMiss may be preferable due to its targeted selection. As demonstrated in Table 2, the aforementioned distinctions between the NearMiss method and RUS translate to marginally superior performance in terms of balance accuracy for the NearMiss method. Consequently, NearMiss was chosen as the preferred technique for application within study case 3. Lastly, the findings from study case 3, shown in Table 3, can be categorized into three groups. Firstly, the least effective methods include SMODB and DE_oversampling, with accuracy values of 69.3% and 70.8%, respectively. These findings underscore the importance of methodological selection and optimization in achieving desired outcomes, emphasizing the need for further exploration and refinement in algorithmic approaches. Secondly, methods falling within a moderate accuracy range of 80.4% to 87.9% form the second group. Within this range (\(\approx\)7.5%), six methods achieve an accuracy of 87%, three methods attain 86%, one method reaches 83%, and another scores 80.4%. The best methods in this group are Supervised_SMOTE and Cluster_SMOTE. Lastly, the final group comprises methods that have demonstrated the most favorable outcomes. Specifically, the DBSMOTE, CCR, and NDO_Sampling methods achieved accuracy scores of 88.4%, 89.1%, and 91.2%, respectively. These methods not only showcase superior balance accuracy but also demonstrate robustness and efficacy in addressing the given problem. One group of SMOTE variants exhibits high specificity but lower sensitivity. In other words, these methods achieve highly accurate classification in normative cases, at the expense of performance in identifying SMS cases. This group includes G_SMOTE, Assembled_SMOTE, Supervised_SMOTE, and Lee, all of which demonstrate specificity values exceeding 97%. Conversely, another group of methods achieves superior sensitivity, meaning they more effectively identify SMS cases of the minority class, but this success comes at the cost of correctly classifying majority class cases. LVQ_SMOTE and DBSMOTE are prime examples within this group, with sensitivity values of 83% and 80%, respectively. Figure 3 illustrates the interplay among sensitivity, specificity, and the equilibrium of balance accuracy, wherein sensitivity denotes the proportion of properly classified SMS cases, and specificity represents the accurate classification of normotypic individuals. The graphical depiction is organized clockwise, aligning with descending order based on the balance accuracy rates. Notably, the NDO_sampling method attains the highest value, while the SMODB method exhibits the lowest value in this continuum. NDO_Sampling exhibits exceptional specificity, consistently exceeding 93% across the ten iterations.