  Dementia is a leading cause of diseases for the elderly. Early diagnosis is
very important for the elderly living with dementias. In this paper, we propose
a method for dementia diagnosis by predicting MMSE score from finger-tapping
measurement with machine learning pipeline. Based on measurement of finger
tapping movement, the pipeline is first to select finger-tapping attributes
with copula entropy and then to predict MMSE score from the selected attributes
with predictive models. Experiments on real world data show that the predictive
models such developed present good prediction performance. As a byproduct, the
associations between certain finger-tapping attributes ('Number of taps',
'Average of intervals', and 'Frequency of taps' of both hands of bimanual
in-phase task) and MMSE score are discovered with copula entropy, which may be
interpreted as the biological relationship between cognitive ability and motor
ability and therefore makes the predictive models explainable. The selected
finger-tapping attributes can be considered as dementia biomarkers.
