Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.

中文翻译：

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使用临床特征预测阿尔茨海默病转变的多专家集成系统

阿尔茨海默氏病 (AD) 诊断通常需要侵入性检查（例如，酒精分析）、昂贵的工具（例如，脑成像）和高度专业化的人员。当疾病已经造成严重的脑损伤并且临床症状开始明显时，通常可以确定诊断。取而代之的是，在出现明显症状之前数年及早识别出患有 AD 高风险的受试者的可及且低成本的方法是为更有效的临床管理、治疗和护理计划提供关键时间窗口的基础。本文提出了一种基于集成的机器学习算法，用于预测从第一个明显迹象开始的 9 年内的 AD 发展，并且仅使用五个可以通过神经心理学测试轻松检测到的临床特征。该系统的验证涉及从 ADNI 开放数据集中抽取的健康个体和轻度认知障碍 (MCI) 患者，这与之前仅考虑 MCI 的研究不同。与其他类似解决方案相比，该系统显示出更高水平的平衡准确性、阴性预测值和特异性。这些结果代表了进一步重要的一步，即构建一种基于机器学习的预防性快速筛查工具，以用作常规医疗保健筛查的一部分。