Analysis of Predictive Factors for Cognitive Impairment in the Elderly using Logistic Regression and Decision Tree Analysis
Abstract
No studies have analyzed the path of predicting the experience of cognitive dysfunction by considering various characteristics in elderly, especially focusing on sleep duration. Thus, this study aimed to predict the experience of cognitive dysfunction according to sleep duration in older individuals. This cross-sectional study used data from 3,361 older individuals from the 2021 Community Health Survey (CHS). Participants were included in two groups according to their experience of cognitive dysfunction (yes or no). Sleep duration was categorized into the following three groups: lack of sleep(<6h), normal sleep (6 to <10h), and oversleep (≥10h). Decision tree and logistic regression analyses were used to identify factors related to cognitive dysfunction in elderly. According to the decision model, those who slept for ≥10h had depression and experienced the highest rate (89.2%)of cognitive dysfunction. In contrast, people aged 65-74 years with a lack of sleep or average sleep duration and low stress levels were the least likely to experience cognitive dysfunction (63.0%). Older individuals who were asleep for ≥10h and had depression showed the highest rate of cognitive dysfunction. Community-based programs to improve cognition in the elderly or healthcare providers caring for the elderly need to continuously assess and consider their age, sleep time, and depression to prevent and manage cognition dysfunction in elderly.
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