Data sets for identifying Alzheimer's disease (AD) are often relatively sparse, which limits their ability to train generalizable models. Here, we augment such a data set, DementiaBank, with each of two normative data sets, the Wisconsin Longitudinal Study and Talk2Me, each of which employs a speech-based picture-description assessment. Through minority class oversampling with ADASYN, we outperform state-of-the-art results in binary classification of people with and without AD in DementiaBank. This work highlights the effectiveness of combining sparse and difficult-to-acquire patient data with relatively large and easily accessible normative datasets.
@article{arxiv.1712.00069,
title = {On the importance of normative data in speech-based assessment},
author = {Zeinab Noorian and Chloé Pou-Prom and Frank Rudzicz},
journal= {arXiv preprint arXiv:1712.00069},
year = {2017}
}