  Predicting Mild Cognitive Impairment (MCI) is currently a challenge as
existing diagnostic criteria rely on neuropsychological examinations. Automated
Machine Learning (ML) models that are trained on verbal utterances of MCI
patients can aid diagnosis. Using a combination of skip-gram features, our
model learned several linguistic biomarkers to distinguish between 19 patients
with MCI and 19 healthy control individuals from the DementiaBank language
transcript clinical dataset. Results show that a model with compound of
skip-grams has better AUC and could help ML prediction on small MCI data
sample.
