# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials
### Methodology  
Figure 2 depicts the application's comprehensive workflow, which can be broken down into seven distinct phases. This research work utilizes three study cases. Study cases 1 and 2 delve into specific aspects of a seven-stage process, focusing on all phases except phases 4 and 5, respectively. In contrast, study case 3 offers a comprehensive examination by encompassing all seven stages. In this final study case, the computation time, which was the longest, was approximately 9 h and was executed on an instance of a supercluster with the following features: 32 VCPU, 60.5 GB of RAM, and 16 GB of virtual hard disk. Figure 2: Main phases of the application. * Phase 1: Feature extraction involves the automatic extraction of mel-frequency cepstral coefficients (MFCCs) [112] and cepstral peak prominence (CPP) features from audio files in WAV format. MFCCs are effective for tasks like speaker and speech recognition, while CPPs represent the most prominent spectral peaks. The extracted features are transformed into a 100 \(\times\) 21 matrix, with the first 20 columns holding MFCC coefficients and the 21 st column storing the CPP value. This data structure provides valuable insights into the audio signal's characteristics. For a visual representation of this data structure, please refer to Figure 2 phase one. * Phase 2: Stratified K-fold involves separating data into distinct folds before adding synthetic data to prevent data leakage, which can compromise model evaluation. Data leakage occurs when the model accesses test set information during training, leading to inflated performance. To avoid this, a 10-fold cross-validation approach is used, partitioning data into ten equally sized folds at the individual level. This ensures that all audio samples from a single participant are in the same fold, preventing the model from encountering information about a participant during training that it might see during validation. This approach provides a more reliable evaluation of the model's ability to generalize to unseen data. * Phase 3: Grouping method expands and diversifies datasets in machine learning but can introduce redundant information when synthetic samples closely resemble existing data. To address this, a stratified oversampling technique focusing on redundancy reduction is proposed. This process is applied to both the SMS and control groups, ensuring dataset enrichment while minimizing redundancy. If an individual has more than five original samples, the extras are excluded from oversampling but are added to the training set later. This ensures balanced representation and reduces redundancy. A nonstratified approach, like SMOTE, could introduce bias by selecting nearest neighbors from the same individual, leading to redundant information. Stratified oversampling avoids this by ensuring that each fold during cross-validation contains no more than one original sample per individual, promoting a diverse pool of nearest neighbors and enriching the dataset while mitigating redundancy. * Phase 4: Oversampling. During this stage, the focus shifts to augmenting synthetic data within each group established earlier. The goal is to create a comprehensive and diverse final training dataset, enhancing the robustness of analyses and model performance. Tailored data augmentation methods are applied to each group. Maintaining a balanced sample distribution is crucial, with both original and augmented samples consistently at 75%. This balance prevents noise or redundancy, preserving the integrity of analyses. Initially, the normotypic group had 1302 samples, and the SMS group had 83. After augmentation, the dataset totaled 996 samples (in study case 1 and study case 3), highlighting the process's effectiveness in increasing volume and diversity. All groups are then combined into a unified dataset. Subsequent undersampling is conducted globally, reducing the risk of discarding relevant samples. * Phase 5: Undersampling.