Evaluating model for the risk factors of mild cognitive impairment among rural elderly in Guizhou Province
10.3760/cma.j.cn371468-20230619-00288
- VernacularTitle:贵州农村老年人轻度认知功能损害患病风险评估模型研究
- Author:
Xiaoling CHEN
1
;
Qingyue WU
;
Jingyuan YANG
;
Weina XUE
;
Xi LONG
;
Xing YANG
Author Information
1. 贵州医科大学公共卫生与健康学院，贵阳 550025
- Keywords:
Machine learning;
Elderly;
Mild cognitive impairment;
Evaluation model;
Lifestyle for brain health
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2023;32(9):780-786
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the lifestyle for brain health related factors and demographic characteristics through machine learning to achieve the assessing effect of mild cognitive impairement prevalence risk among rural elderly people in Guizhou.Methods:From July to August 2019, a multi-stage cluster random sampling method was used to select 1 235 rural elderly people aged 60 years and above in Guizhou Province as the subjects, and the investigation was performed with questionnaire and physical examination.The Mini-Mental State Examination (MMSE) was used to assess cognitive function, and the important features and optimal number of features based on 12 LIBRA factors and 4 demographic characteristics were selected.Logistic regression and random forest algorithm were used to establish a evaluation model for the risk of mild cognitive impairment in the elderly.The evaluation efficacy of the model was also assessed using a combination of precision, accuracy, sensitivity, specificity, F1 score and area under curve (AUC) of receiver operating characteristic curve, and the Delong method was used to check the difference of AUC values between the two models.Results:A total of 291 subjects were diagnosed with mild cognitive impairment, with a detection rate of 23.56% (291/1 235). The AUC values of logistic regression and random forest models evaluating the risk of mild cognitive impairment in the rural elderly were 0.758 and 0.820, respectively, and the differences were statistically significant(both P<0.05). The random forest model had better evaluations with an accuracy of 0.823, precision of 0.805, sensitivity of 0.874, specificity of 0.767 and F1 score of 0.838, all of which were better than those of the logistic regression model.And the random forest model was also more stable after 10-fold cross-validation. Conclusion:The lifestyle for brain health related factors combined with demographic characteristics can more accurately evaluate the risk of mild cognitive impairment among rural elderly people in Guizhou.The random forest model is better than the logistic regression model.
