Cascade classification for diagnosing dementia

Dementia is a syndrome caused by a chronic or progressive disease of the brain, which affects memory, orientation, thinking, calculation, learning ability and language. The Clock Drawing Test (CDT) and Mini Mental State Examination (MMSE) are well-known cognitive assessment tests. A known obstacle to the wider usage of the CDT assessments is the scoring and interpretation of the results. This paper introduces a novel cascade CDT classifier, which can help in the diagnosis of three stages of dementia. The data used in this research are 604 clock drawings produced by patients and healthy individuals. The study employs 47 visual features, which are selected following a comprehensive analysis of the available data and the most common CDT scoring systems reported in the medical literature. These features are used to build a new digitized dataset needed to train and validate the proposed classifier. The results show significant improvement of 6.8% in differentiating between three levels of dementia (normal/functional, mild cognitive impairment/mild dementia, and moderate/severe dementia) when compared to a single stage classifier. In particular, the results show classification accuracy of over 89% when discriminating between normal and abnormal conditions only.