# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 6 Conclusions  
This groundbreaking research utilizes a neural network model, analyzing phonation data to identify a rare genetic syndrome. Furthermore, to address the challenge of imbalanced datasets, the research work incorporated sixteen distinct variations of the SMOTE. Each SMOTE variation was employed to generate synthetic data points for the minority class, effectively balancing the class distribution within the dataset. The study achieved high accuracy in detecting the syndrome (SMS) by employing synthetically generated data (through oversampling and undersampling) alongside a CNN. This manuscript highlights the potential of voice analysis, using only two types of acoustic features: MFCC and CPP, and deep learning techniques as a noninvasive and valuable tool. This finding holds promise for improving the identification of this complex syndrome and potentially other rare diseases. This research work presents three study cases. The first study case investigates the use of two standard oversampling techniques (SMOTE and ROS). Both techniques achieved similar results, with a classification rate of 88%. The second study case focuses on the application of two undersampling techniques (NearMiss and RUS). This approach resulted in a significant decline in performance compared to the previous study case, with NearMiss achieving only a 78% classification rate. Finally, a comprehensive study case was conducted, employing 16 oversampling techniques and one undersampling technique. This study case yielded the highest accuracy rate, with NDO_sampling and NearMiss achieving approximately 92% accuracy. It should be highlighted that the overarching objective of this study centers on determining the impact of oversampling and undersampling techniques on the accuracy rate within imbalanced datasets. This elaborate work not only presents a significant advancement in the classification of patients with SMS through the analysis of voice samples but also paves the way for exploring similar methodologies in the classification and study of other rare genetic syndromes. Conditions such as [PERSON] syndrome, [PERSON] syndrome, Fragile X syndrome, and [PERSON] syndrome stand as examples of rare genetic disorders that could potentially benefit from similar voice-based approaches. By extending the scope of research to encompass these conditions, the broader research community can envisage new avenues for early detection, classification, and understanding of these complex disorders. Furthermore, this work can be extended by exploring hybrid models combining voice and image analysis. The authors aim to determine if adding visual information can enhance current models, especially for tasks requiring deeper contextual understanding. To sum up, this research work demonstrates the feasibility of using CNNs in conjunction with data augmentation techniques to develop an accurate classification system for SMS. The obtained results contribute to the field of computer-aided diagnosis and open new avenues for the development of voice-based screening tools. While the results are promising, further studies with larger and more diverse datasets are needed to validate the generalization of the models. Additionally, exploring other neural network architectures and feature extraction techniques could further improve classification performance. Nevertheless, the results of this research suggest that CNNs, combined with data augmentation techniques, represent a promising tool for the diagnosis of SMS. Conceptualization, E.N.-V.; methodology, E.N.-V. and R.F.-R.; software, R.F.-R., E.N.-V. and F.D.-M.; validation, A.A.-M. and E.G.-H.; formal analysis, A.A.-M.; investigation, E.N.-V. and I.H.-d.; resources, I.H.-d., E.N.-V. and R.F.-R.; data curation, I.H.-d.; writing--original draft preparation, E.N.-V., R.F.-R. and I.H.-d.; writing--review and editing, E.G.-H. and N.H.-K.; visualization, R.F.-R.; supervision, E.G.-H., C.C. and N.H.-K.; project administration, C.C. and R.M.-O. ; funding acquisition, R.M.-O.