Abstract
AbstractDementia 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’ and ‘SD of inter-tapping interval’) 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.
Publisher
Cold Spring Harbor Laboratory
Reference33 articles.
1. United Nations. World Population Prospects. 2017.
2. World Health Organization. Global Health Estimates Summary Tables: Deaths by Cause, Age and Sex by various regional grouping. July 2013.
3. Alzheimer’s Disease International. World Alzheimer Report 2009. 2009.
4. Prince M , Bryce R , Ferri C. World Alzheimer Report 2011: The benefits of early diagnosis and intervention. Alzheimer’s Disease International, 2011.
5. Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review) [RETIRED]
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献