Automatic Identification of Alzheimer's Disease using Lexical Features
  extracted from Language Samples
M. Zakaria Kurdi
Objective: this study has a twofold goal. First, it aims to improve the
understanding of the impact of Dementia of type Alzheimer's Disease (AD) on
different aspects of the lexicon. Second, it aims to demonstrate that such
aspects of the lexicon, when used as features of a machine learning classifier,
can help achieve state-of-the-art performance in automatically identifying
language samples produced by patients with AD. Methods: data is derived from
the ADDreSS challenge, which is a part of the DementiaBank corpus. The used
dataset consists of transcripts of Cookie Theft picture descriptions, produced
by 54 subjects in the training part and 24 subjects in the test part. The
number of narrative samples is 108 in the training set and 48 in the test set.
First, the impact of AD on 99 selected lexical features is studied using both
the training and testing parts of the dataset. Then some machine learning
experiments were conducted on the task of classifying transcribed speech
samples with text samples that were produced by people with AD from those
produced by normal subjects. Several experiments were conducted to compare the
different areas of lexical complexity, identify the subset of features that
help achieve optimal performance, and study the impact of the size of the input
on the classification. To evaluate the generalization of the models built on
narrative speech, two generalization tests were conducted using written data
from two British authors, Iris Murdoch and Agatha Christie, and the
transcription of some speeches by former President Ronald Reagan. Results:
using lexical features only, state-of-the-art classification, F1 and
accuracies, of over 91% were achieved in categorizing language samples produced
by individuals with AD from the ones produced by healthy control subjects. This
confirms the substantial impact of AD on lexicon processing.