Binary Classification Prediction of Cognitive Status Using Sleep Activity Data

Abstract

The sleep patterns of patients with dementia have been observed to be affected. This study aims to investigate the feasibility of developing a machine learning model that can classify scores of dementia screening tests based on sleep activity data that can be collected with minimal burden on participants. Data on sleep activity was collected from 124 elderly patients with varying levels of cognitive ability. The Mini Mental State Estimation (MMSE) cognitive test scores were used to determine the cognitive states of the patients. To classify the dementia scale and identify individuals with low-MMSE, we employed an efficient sequence model to capture time-series changes in sleep activity. Using LSTM models, a maximum macro F1 score of 0.67 was achieved in the bina