  We present a practical health-theme machine learning (ML) application
concerning `AI for social good' domain for `Producing Good Outcomes' track. In
particular, the solution is concerning the problem of a potential elderly adult
dementia onset prediction in aging societies. The paper discusses our attempt
and encouraging preliminary study results of behavioral responses analysis in a
working memory-based emotional evaluation experiment. We focus on the
development of digital biomarkers for dementia progress detection and
monitoring. We present a behavioral data collection concept for a subsequent
AI-based application together with a range of regression encouraging results of
Montreal Cognitive Assessment (MoCA) scores in the leave-one-subject-out
cross-validation setup. The regressor input variables include experimental
subject's emotional valence and arousal recognition responses, as well as
reaction times, together with self-reported education levels and ages, obtained
from a group of twenty older adults taking part in the reported data collection
project. The presented results showcase the potential social benefits of
artificial intelligence application for elderly and establish a step forward to
develop ML approaches, for the subsequent application of simple behavioral
objective testing for dementia onset diagnostics replacing subjective MoCA.
