基于机器学习的轻度认知功能障碍筛查研究

Study of a screening system for mild cognitive impairment based on machine learning model

目的·评价项目研制的可用于轻度认知功能障碍筛查的电子化认知评估系统的信度和效度,构建机器学习法判定模型并评估筛查效果。方法·采用分层随机的方法在上海和河南农村的社区、老年护理院及专科门诊抽取55岁以上的符合标准的老年人,由经过严格培训、操作规范的调查员对研究对象进行蒙特利尔认知评估量表(Montreal Cognitive Assessment,MoCA)的现场测试。电子化认知评估系统信度评价采用内部一致性系数,效度评价采用因子分析;以MoCA评估结果作为标准,使用分类准确率和曲线下面积(area under curve,AUC)比较朴素贝叶斯、随机森林、Logistic回归和K-邻近4种机器学习算法的分类效果。结果·研究的359名对象中,年龄中位数为63岁,82.80%为中学及以下学历;根据MoCA评分,可能患有轻度认知功能障碍的有147名。电子化认知评估系统的Cronbach'sα为0.84,KMO为0.78,Bartlett's球形检验P<0.05,共提取13个公因子,累计方差贡献率为75.10%。最优朴素贝叶斯分类模型的分类准确率为88.05%,AUC为0.941。结论·该电子化认知评估系统具有良好的信度、效度及分类效果,利用朴素贝叶斯分类模型分类准确度较高。

Objective·To evaluate the reliability and validity of a computerized cognitive assessment system designed for screening mild cognitive impairment(MCI),and compare the screening accuracy among constructed different machine learning classification models.Methods·A group of random stratified samples of over 55 years old residents in the communities,nursing homes and memory-clinics from Shanghai and Henan were selected to assess their cognitive status using Montreal Cognitive Assessment(MoCA)by well-trained investigators.The reliability and validity were assessed by intrinsic consistency analysis and factor analysis,respectively.Taking the results of MoCA as standards,four machine learning classification algorithms,i.e.,naive Bayesian classification model,random forest classifier,Logistic regression classifier,and K-nearest neighbor classifier,were compared in accuracy and area under curve(AUC).Results·A total of 359 participants were included,the median age of whom was 63 years old.And 82.80%of them were secondary school graduates or below.According to the results of MoCA,147 of them might be MCI.The Cronbach'sαand KMO of this system were 0.84 and 0.78,respectively;Bartlett's sphericity test was significant(P<0.05);thirteen common factors could explain 75.10%of the system.The best classification model was naive Bayesian classification model,and its accuracy and AUC were 88.05%and 0.941,respectively.Conclusion·The new designed computerized cognitive assessment system has been proved to be reliable and valid.The naive Bayesian classification model has good classification accuracy.