基于集成机器学习预测主观认知下降向轻度认知障碍和阿尔茨海默病痴呆的转化。

Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning.

机构信息

Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.

Memory Unit, Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain.

出版信息

J Alzheimers Dis. 2023;93(1):125-140. doi: 10.3233/JAD-221002.

DOI:10.3233/JAD-221002

PMID:36938735

Abstract

BACKGROUND

Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge.

OBJECTIVE

To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD.

METHODS

Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model.

RESULTS

Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11).

CONCLUSION

A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

摘要

背景

主观认知下降（SCD）可能代表阿尔茨海默病（AD）的临床前阶段。预测 SCD 患者的进展对于 AD 相关研究非常重要，但仍然是一个挑战。

目的

开发和实施集成机器学习（ML）算法，以识别有进展为轻度认知障碍（MCI）或 AD 风险的 SCD 患者。

方法

纳入 99 例 SCD 患者。32 例进展为 MCI/AD，67 例稳定。为了最小化类不平衡的影响，对两类进行了平衡，并以敏感性作为评价指标。使用社会人口统计学和临床信息、简易精神状态检查和老年抑郁量表（GDS）评分（特征集 1a）、社会人口统计学特征和神经心理学测试评分（特征集 1b）和区域磁共振成像灰质体积（特征集 2）开发了袋装和提升 ML 模型。结合最相关的变量以找到最佳模型。

结果

特征集 1a 和 2 获得了良好的预测性能。最相关的变量（变量重要性超过 20%）为：年龄、GDS 和四个感兴趣的皮质区域的灰质体积。它们的组合提供了最佳分类性能（最高敏感性和特异性）的集成 ML 模型，即对少数类进行过采样的极端梯度提升，其性能指标为：敏感性=1.00，特异性=0.92 和曲线下面积=0.96。基于 50 次随机训练/测试拆分的中位数值为敏感性=0.83（四分位距（IQR）=0.17），特异性=0.77（IQR=0.23）和曲线下面积=0.75（IQR=0.11）。