Influence of Lifestyles on Mild Cognitive Impairment: A Decision Tree Model Study

Abstract

Objective: To explore the effects of different lifestyle choices on mild cognitive impairment (MCI) and to establish a decision tree model to analyse their predictive significance on the incidence of MCI.

Methods: Study participants were recruited from geriatric and physical examination centres from October 2015 to October 2019: 330 MCI patients and 295 normal cognitive (NC) patients. Cognitive function was evaluated by the Mini-Mental State Examination Scale (MMSE) and Clinical Dementia Scale (CDR), while the Barthel Index (BI) was used to evaluate life ability. Statistical analysis included the χ ² test, logistic regression, and decision tree. The ROC curve was drawn to evaluate the predictive ability of the decision tree model.

Results: Logistic regression analysis showed that low education, living alone, smoking, and a high-fat diet were risk factors for MCI, while young age, tea drinking, afternoon naps, social engagement, and hobbies were protective factors for MCI. Social engagement, a high-fat diet, hobbies, living condition, tea drinking, and smoking entered all nodes of the decision tree model, with social engagement as the root node variable. The importance of predictive variables in the decision tree model showed social engagement, a high-fat diet, tea drinking, hobbies, living condition, and smoking as 33.57%, 27.74%, 22.14%, 11.94%, 4.61%, and 0%, respectively. The area under the ROC curve predicted by the decision tree model was 0.827 (95% CI: 0.795~0.856).

Conclusion: The decision tree model has good predictive ability. MCI was closely related to lifestyle; social engagement was the most important factor in predicting the occurrence of MCI.

Keywords: behaviours habit; decision tree model; influencing factors; lifestyle; mild cognitive impairment.

Figure 1

Decision tree model of the influence of lifestyles on MCI. This tree is generated by the software through 10 cross-validations. Each box is numbered sequentially. The blue icon represents MCI, and the red icon represents NC. Each box has its own percentage and specific number of examples. Below each box is the influence factor of the next classification; at the top of each box is the corresponding category of influence factor.

Figure 2

Importance of predictive variables in decision tree model. This figure shows the importance of each influence factor to the result prediction in the decision tree model. As a root node variable, social engagement is the most important predictive variable.

Figure 3

ROC curve of MCI occurrence predicted by decision tree model. The broken line in the figure represents the ability of the decision tree model to predict the occurrence of MCI. The AUC was 0.827, P < 0.001.