The presented paper discusses a practical application of machine learning ML in the so-called 'AI for social good' domain and in particular concerning the problem of a potential elderly adult dementia onset prediction. An increase in dementia cases is producing a significant medical and economic weight in many countries. Approximately 47 million older adults live with a dementia spectrum of neurocognitive disorders, according to an up-to-date statement of the World Health Organization WHO , and this amount will triple within the next thirty years.
This growing problem calls for possible application of AI-based technologies to support early diagnostics for cognitive interventions and a subsequent mental wellbeing monitoring as well as maintenance with so-called 'digital-pharma' or 'beyond a pill' therapeutical strategies.
The paper explains our attempt and encouraging preliminary study results of behavioral responses analysis in a facial emotion implicit-short-term-memory learning and evaluation experiment.
We present results of various shallow and deep learning machine learning models for digital biomarkers of dementia progress detection and monitoring. The classifier input features consist of an older adult emotional valence and arousal recognition responses, together with reaction times, as well as with self-reported university-level degree education and age, as obtained from a group of 35 older adults participating voluntarily in the reported dementia biomarker development project.
The presented results showcase the inherent social benefits of artificial intelligence AI utilization for the elderly and establish a step forward to advance machine learning ML approaches for the subsequent employment of simple behavioral examination for MCI and dementia onset diagnostics. Clinical relevance- This manuscript establishes a behavioral and cognitive biomarker candidate potentially substituting a Montreal Cognitive Assessment MoCA evaluation without a paper and pencil test.
