Orlunwo Placida Orochi, Onuodu Friday Eleonu
Ignatius Ajuru University of Education.
Res Sq. 2024 Dec 23:rs.3.rs-5644910. doi: 10.21203/rs.3.rs-5644910/v1.
Alzheimer's disease (AD) is a progressive neurological condition characterized by a loss in cognitive functions, with no disease-modifying medication now available. It is crucial for early detection and treatment of Alzheimer's disease before clinical manifestation. The stage between cognitively healthy older persons and AD is known as mild cognitive impairment (MCI). To predict the transition from one-stage MCI to probable AD, five ensemble learning approach was used (Stacking, Gradient boost Bagging, Adaptive boost and Voting), an integrated model that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The adaptive boost, stacking and bagging ensemble approach has shown potential to identify those at risk of developing Alzheimer's disease, this would benefit them the most from a clinical trial or to use as a stratification approach inside clinical trials.
阿尔茨海默病（AD）是一种进行性神经疾病，其特征为认知功能丧失，目前尚无疾病修饰药物。在临床表现出现之前对阿尔茨海默病进行早期检测和治疗至关重要。认知健康的老年人与AD之间的阶段称为轻度认知障碍（MCI）。为了预测从单阶段MCI向可能的AD的转变，使用了五种集成学习方法（堆叠、梯度提升、装袋、自适应提升和投票），该集成模型不仅结合了基线时的横断面神经影像生物标志物，还结合了来自阿尔茨海默病神经影像倡议队列（ADNI）的纵向脑脊液（CSF）和认知表现生物标志物。自适应提升、堆叠和装袋集成方法已显示出识别有患阿尔茨海默病风险者的潜力，这将使他们从临床试验中获益最大，或用作临床试验中的分层方法。
