To explore the construction of a predictive model for cognitive impairment in elderly patients with Alzheimer's disease (AD) based on influencing factors and the application value of electronic cognitive rehabilitation training.

A total of 102 elderly patients with AD cognitive dysfunction admitted to the Second Department of Geriatrics of Aerospace Center Hospital from January 2019 to January 2020 were selected as the observation group, which were divided into two subgroups according to different treatment methods, including conventional cognitive intervention group (51 cases) and electronic cognitive rehabilitation training group (51 cases). The 50 elderly patients with normal diagnosis in the same period were selected as the control group. Univariate Logistic regression analysis was used to construct a predictive model of cognitive dysfunction in aged AD based on protective factors. Cognitive function and daily living performance were assessed using the Montreal cognitive assessment scale (MoCA) and the activities of daily living (ADL) scale.

The differences between observation and control groups were statistically significant in age, occupational status, proportion of hyperlipidemia and proportion of aspirin (P<0.05); Logistic regression analysis showed that mental work was a risk factor for cognitive dysfunction in aged AD (OR=0.348, P<0.05); The ROC curve was used to evaluate the predictive model of cognitive dysfunction in elderly AD, with a sensitivity of 95.06%, a specificity of 92.01%, and a area under the curve of 0.908 (95%CI: 0.879-1.000). The MoCA scores of both groups of patients after intervention were higher than before intervention, and that of the electronic cognitive rehabilitation training group was higher than that of the conventional cognitive intervention group, with a statistically significant difference (P<0.05). The ADL scores of the patients in the 2 groups were lower than those before the intervention, and the electronic cognitive rehabilitation training group was lower than the conventional cognitive intervention group, and the differences were statistically significant (P<0.05).

The prediction model shows that occupational status is the influencing factor of cognitive dysfunction in elderly AD, and mental work is a risk factor of AD in the elderly. Electronic cognitive rehabilitation training can improve the cognitive function and daily activity ability of elderly patients with cognitive dysfunction.