Background:The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer’s disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection. Objective:The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia. Methods:We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia. Results:The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques. Conclusion:These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM’s necessity or value for many clinical researchers.

中文翻译：

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先验知识在从轻度认知障碍到阿尔茨海默病转变的统计分类中的作用

背景：轻度认知障碍（MCI）向痴呆的转变对阿尔茨海默病及相关痴呆的临床研究具有重要意义。这种现象也可作为定量方法研究人员开发新分类方法的宝贵数据来源。然而，机器学习 (ML) 分类方法的发展可能会错误地导致许多临床研究人员低估逻辑回归 (LR) 的价值，逻辑回归 (LR) 通常表明分类精度与其他 ML 方法相当或优于其他 ML 方法。此外，当面临许多可用于对转换进行分类的潜在特征时，临床研究人员通常不知道不同变量选择方法的相对价值。客观的：本研究试图在预测从 MCI 到痴呆症的转换的背景下，比较不同的统计分类方法和自动和理论指导的特征选择技术。方法：我们使用来自阿尔茨海默病神经影像学倡议 (ADNI) 的数据来评估自动特征预选对 LR 和支持向量机 (SVM) 分类方法的不同影响，对从 MCI 到痴呆症的转换进行分类。结果：目前的研究结果表明，使用用户指导的、临床知情的预选技术与算法特征选择技术相比，如何实现相似的性能。结论：这些结果表明，虽然 SVM 和其他 ML 技术能够进行相对准确的分类，但 LR 通常可以达到类似或更高的准确率，