Alzheimer's disease (AD) is a neurodegenerative condition driven by a multifactorial etiology. We employed a machine learning (ML) based algorithm and the wealth of information offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate the relative contribution of clinically relevant factors for identifying subjects affected by Mild Cognitive Impairment (MCI), a transitional status between healthy aging and dementia. Our ML-based Random Forest (RF) algorithm did not help predict clinical outcomes and the AD conversion of MCI subjects. On the other hand, non-converting (ncMCI) subjects were correctly classified and predicted. Two neuropsychological tests, the FAQ and ADAS13, were the most relevant features used for the classification and prediction of younger, under 70, ncMCI subjects. Structural MRI data combined with systemic parameters and the cardiovascular status were instead the most critical factors for the classification of over 70 ncMCI subjects. Our results support the notion that AD is not an organ-specific condition and results from pathological processes inside and outside the Central Nervous System.

中文翻译：

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基于机器学习的整体方法在阿尔茨海默氏病谱内进行诊断

阿尔茨海默氏病（AD）是由多种病因引起的神经退行性疾病。我们采用了基于机器学习（ML）的算法，并使用了阿尔茨海默氏病神经影像学计划（ADNI）数据库提供的大量信息来研究临床相关因素的相对贡献，以识别受轻度认知障碍（MCI）影响的受试者在健康的衰老和痴呆之间。我们基于ML的随机森林（RF）算法无法帮助预测MCI受试者的临床结果和AD转换。另一方面，正确地分类和预测了非转化（ncMCI）受试者。两种神经心理学测试分别是FAQ和ADAS13，它们是用于分类和预测70岁以下ncMCI受试者中最相关的功能。相反，结构性MRI数据结合全身参数和心血管状况是对70多个ncMCI受试者进行分类的最关键因素。我们的结果支持以下观点：AD不是器官特异性疾病，而是中枢神经系统内部和外部的病理过程导致的。