Dementia is not a specific disease, but a general term for age-related decline or loss of memory, cognitive abilities including problem solving and decision-making, and one’s own language, which significantly interfere with daily life. Researchers around the world have developed ways to automate the diagnosis of dementia through the use of machine learning and data mining approaches. The aim of this research project is to design and develop a day-to-day activity prediction algorithm in order to accurately identify and differentiate the dementia affected patients from the healthy subjects, to ensure early diagnosis of dementia development. This research advocates a novel algorithm called ‘Sequence Prediction via All Discoverable Episodes (SPADE)’ as a statistical tool to map activities of daily life (ADLs) in different groups of people in order to develop a unique parameter for precise diagnosis. The results of our experiment demonstrated a significant difference (i.e. 11 %) in the sequence prediction peak accuracy between the healthy subjects and the residents with dementia. SPADE demonstrated an adequate accuracy (i.e. 80 % on average), with an improvement of about 12 % compared to the performance of M-SPEED in inferring future occurrences of activities. It is thus evident that the algorithms for activity predictions show promise for early detection of dementia symptoms without the use of any expensive clinical procedure.

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Engineering, Electrical Engineering, Control Engineering, Metrology and Testing