Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia
Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia
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DOI：
10.1002/gps.5090
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发表时间：
2019-07-01
影响因子：
4
通讯作者：
Juncos-Rabadan, Onesimo
中科院分区：
文献类型：
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作者：
Facal, David;Valladares-Rodriguez, Sonia;Juncos-Rabadan, Onesimo
目的探讨认知储备（CR）在轻度认知损害（MCI）向痴呆转化中的作用。我们使用传统和机器学习（ML）技术来比较转换者和非转换者的参与者。我们还讨论了CR代理与ML模型性能相关的预测价值。方法共有169名参与者完成了纵向研究。根据Petersen诊断标准，参与者被分为对照组和三个MCI亚组。使用9种ML分类技术比较了参与者的信息。七个相关的性能指标进行了计算，以评估转换器和nonconverter参与者的预测的准确性。ML算法应用于社会人口统计学，基本健康和CR代理数据，可以预测转化为痴呆症。性能最好的模型是梯度提升分类器（准确度（ACC）= 0.93; F1 = 0.86，Cohen kappa = 0.82）和随机森林分类器（ACC = 0.92; F1 = 0.79，Cohen kappa = 0.71）。ML技术的使用证实了CR作为转化为痴呆症的介导者的保护作用，即受教育年限更长、词汇得分更高的参与者存活时间更长，而不会发生痴呆症。结论我们使用ML方法来探讨CR在MCI向痴呆转化中的作用。研究结果表明，ML算法在认知老化和CR研究中检测转化为痴呆风险的潜在价值。需要进一步的研究来开发一个基于ML的程序，可以用来进行强大的预测。
Objectives The overall aim of the present study was to explore the role of cognitive reserve (CR) in the conversion from mild cognitive impairment (MCI) to dementia. We used traditional and machine learning (ML) techniques to compare converter and nonconverter participants. We also discuss the predictive value of CR proxies in relation to the ML model performance. Methods In total, 169 participants completed the longitudinal study. Participants were divided into a control group and three MCI subgroups, according to the Petersen criteria for diagnosis. Information about the participants was compared using nine ML classification techniques. Seven relevant performance metrics were computed in order to evaluate the accuracy of prediction regarding converter and nonconverter participants. Results ML algorithms applied to socio-demographic, basic health, and CR proxy data enabled prediction of conversion to dementia. The best performing models were the gradient boosting classifier (accuracy (ACC) = 0.93; F1 = 0.86, and Cohen kappa = 0.82) and random forest classifier (ACC = 0.92; F1 = 0.79, and Cohen kappa = 0.71). Use of ML techniques corroborated the protective role of CR as a mediator of conversion to dementia, whereby participants with more years of education and higher vocabulary scores survived longer without developing dementia. Conclusions We used ML approaches to explore the role of CR in conversion from MCI to dementia. The findings indicate the potential value of ML algorithms for detecting risk of conversion to dementia in cognitive aging and CR studies. Further research is required to develop an ML-based procedure that can be used to make robust predictions.