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[TD‐P‐017]: DIGITAL ANALYTICS ON SIMPLE DRAWING IS AN EFFECTIVE METHOD FOR DEMENTIA SCREENING
Author(s) -
Tsoi Kelvin,
Wong Michael PF.,
Lam Max WY.,
Wong Adrian,
Kwok Timothy
Publication year - 2017
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2017.06.2613
Subject(s) - montreal cognitive assessment , dementia , test (biology) , logistic regression , analytics , cognition , psychology , computer science , cognitive impairment , artificial intelligence , medicine , disease , machine learning , data mining , psychiatry , paleontology , biology
significant characteristics for each of four populations: aphasic stroke (ST; N1⁄419), primary progressive aphasia (PPA; N1⁄411), mild cognitive impairment and Alzheimer’s disease (MCI/AD; N1⁄49 and N1⁄42, respectively), and healthy elderly controls (CT; N1⁄426). We then use these features to train a machine-learning classifier to correctly distinguish healthy individuals from patients (CT vs. ST+PPA+MCI), MCI/AD patients from STand PA patients, and controls from each individual patient group (e.g., CT vs. ST). Results: Our decision tree model is able to classify CT versus ST+PPA+MCI with 76.1% accuracy. We classify controls from MCI/AD patients with 89.2% accuracy, controls from PPA with 91.9% accuracy, and controls from stroke patients with 71.1% accuracy. Finally, the MCI/AD patients versus the combined stroke and PPA groups were classified with 80.5% accuracy. Word length and filled pauses were found to serve as prominent features in identifying pathology; however, when comparing controls and the MCI/ AD group, acoustic features were selected more often than for the other populations’ feature sets. Conclusions: Binary classification between groups was between 13% and 21% more accurate than baseline values, and 4-way classification was 14.9% better. It appeared that linguistic features yielded better predictions than did the addition of acoustic features. Ongoing work aims to explain these phenomena and further evaluate the possible use of speech to serve as diagnostic criteria.