# Unraveling Functional Dysphagia: A Game-Changing Automated Machine-Learning Diagnostic Approach
## 4 Discussion  
In this study, we utilized AI shallow learners, commonly known as traditional machine learning algorithms, to distinguish between normal subjects and functional dysphagia patients for the first time and achieved high classification accuracies of more than 90%. As mentioned earlier, the diagnosis of functional dysphagia has traditionally been a diagnosis of exclusion. Our data show a prominent distension abnormality in functional dysphagia patients. We achieved sensitivity (recall) of higher than 90% for the FD group, and this separation was mostly observed in the proximal (close the UES) and distal (close to the LES) segments. Our results indicate that adding distension features significantly improves the classification results compared to using only pressure features. It appears that the primary abnormality in functional dysphagia patients lies in the distension phase of peristalsis rather than the contraction phase. The above being said, a significant issue with shallow learners lies in their incapability to capture intricate patterns and connections within data that are not explicitly presented. These models lack the complexity required to comprehend subtle nuances and hierarchical structures within the data, thus making them less suitable for tasks involving unstructured or nonlinear data. Moreover, shallow learners frequently encounter difficulties with feature engineering, necessitating the manual extraction and selection of pertinent features, which can consume time and is susceptible to errors. Additionally, the challenge of limited sample sizes is worth noting. When dealing with small datasets, as in our study, classifiers might struggle to encompass the full spectrum of variability inherent in the underlying data distribution, which can lead to overfitting, where the model learns from noise and outliers in the training data, resulting in poor adaptation to new, unfamiliar data. Another facet of the generalization problem introduces another hurdle, i.e., a classifier that demonstrates proficiency on the training data may not necessarily perform well on new data points. This predicament arises due to the model's limited capacity to grasp intricate relationships within the data, particularly in scenarios involving nonlinear boundaries or spaces with high dimensions. Consequently, the classifier might either fail to capture significant patterns (underfitting) or become overly focused on the training data (overfitting), thereby failing to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**LR**} & \multicolumn{3}{c}{**KNN**} & \multicolumn{3}{c}{**Linear SVM**} & \multicolumn{3}{c}{**RBF SVM**} & \multicolumn{3}{c}{**Random Forest**} \\ \hline \multirow{4}{*}{**Seg** **1**} & \multirow{2}{*}{**\%**} & \multirow{2}{  
generalize to novel instances. In situations where the sample size is substantial, resorting to more advanced classifiers, like ensemble methods or deep learning architectures, could be considered. In our study, due to the limited sample size of the two groups, we aimed to maintain a balance and implemented meticulous regular cross-validation and hyperparameter tuning across the classifiers, which are essential measures to ensure that a classifier can effectively extend its learned patterns to unseen data. As a result, we achieved favorable outcomes. Our study did not ascertain the precise origin of the distension anomaly in the groups of patients that we examined. Another noteworthy aspect to highlight is that our findings reveal the significance of certain characteristics, such as the standard deviation of peak pressure, in distinguishing between the two groups. This contrasts with the Chicago Classification, where such differentiation is not applicable.