Automatic Identification of Behavior Patterns in Mild Cognitive Impairments and Alzheimer’s Disease Based on Activities of Daily Living
The growing number of older adults worldwide places high pressure on identifying dementia at its earliest stages so that early management and intervention strategies could be planned. In this study, we proposed a machine learning based method for automatic identification of behavioral patterns of people with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) through the analysis of data related to their activities of daily living (ADL) collected in two smart home environments. Our method employs first a feature selection technique to extract relevant features for classification and reduce the dimensionality of the data. Then, the output of the feature selection is fed into a random forest classifier for classification. We recruited three groups of participants in our study: healthy older adults, older adults with mild cognitive impairment and older adults with Alzheimer’s disease. We conducted extensive experiments to validate our proposed method. We experimentally showed that our method outperforms state-of-the-art machine learning algorithms.
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