Alzheimer’s disease (AD) and cognitive impairment due to aging are the recently prevailing diseases among aged inhabitants due to an increase in the aging population. Several demographic characters, structural and functional neuroimaging investigations, cardio-vascular studies, neuropsychiatric symptoms, cognitive performances, and biomarkers in cerebrospinal fluids are the various predictors for AD. These input features can be considered for the prediction of symptoms whether they belong to AD or normal cognitive impairment due to aging. In the proposed study, the hypothesis is derived for supervised learning methods such as multivariate linear regression, logistic regression, and SVM. Feature scaling and normalization are performed with features as initial steps for applying the parameters to derive the hypothesis. Performance metrics are analyzed with the implementation results. The present work is applied to 1000 baseline assessment data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) studies that give conversion prediction. The comparison of results in the literature suggests that the efficiency of the proposed study is highly advantageous in differentiating AD pathology from cognitive impairment due to aging.

老年痴呆症（AD）和由于衰老引起的认知障碍是由于老年人口增加而引起的老年人中最近流行的疾病。几种人口统计学特征，结构和功能性神经影像学检查，心血管研究，神经精神症状，认知表现以及脑脊液中的生物标志物是AD的多种预测指标。这些输入功能可以考虑用于预测症状，无论它们属于AD还是由于衰老导致的正常认知障碍。在提出的研究中，假设是针对监督学习方法（例如多元线性回归，逻辑回归和SVM）得出的。以特征为特征执行特征缩放和归一化，作为应用参数以得出假设的初始步骤。性能指标与实施结果一起进行分析。本研究适用于来自阿尔茨海默氏病神经影像学倡议（ADNI）研究的1000个基线评估数据，这些数据给出了转化预测。文献中结果的比较表明，所提出的研究的效率在区分AD病理学与衰老引起的认知障碍方面非常有优势。