  This study evaluates the use of machine learning, specifically the Random
Forest Classifier, to differentiate normal and pathological swallowing sounds.
Employing a commercially available wearable stethoscope, we recorded swallows
from both healthy adults and patients with dysphagia. The analysis revealed
statistically significant differences in acoustic features, such as spectral
crest, and zero-crossing rate between normal and pathological swallows, while
no discriminating differences were demonstrated between different fluidand diet
consistencies. The system demonstrated fair sensitivity (mean plus or minus SD:
74% plus or minus 8%) and specificity (89% plus or minus 6%) for dysphagic
swallows. The model attained an overall accuracy of 83% plus or minus 3%, and
F1 score of 78% plus or minus 5%. These results demonstrate that machine
learning can be a valuable tool in non-invasive dysphagia assessment, although
challenges such as sampling rate limitations and variability in sensitivity and
specificity in discriminating between normal and pathological sounds are noted.
The study underscores the need for further research to optimize these
techniques for clinical use.
