Early diagnosis of Alzheimer's disease is a challenge because the existing methodologies do not identify the patients in their preclinical stage, which can last up to a decade prior to the onset of clinical symptoms. Several research studies demonstrate the potential of cerebrospinal fluid biomarkers, amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. In this work, we used machine learning models to classify different stages of Alzheimer's disease based on the cerebrospinal fluid biomarker levels alone. An electronic health record of patients from the National Alzheimer's Coordinating Centre database was analyzed and the patients were subdivided based on mini-mental state scores and clinical dementia ratings. Statistical and correlation analyses were performed to identify significant differences between the Alzheimer's stages. Afterward, machine learning classifiers including K-Nearest Neighbors, Ensemble Boosted Tree, Ensemble Bagged Tree, Support Vector Machine, Logistic Regression, and Naive Bayes classifiers were employed to classify the Alzheimer's disease stages. The results demonstrate that Ensemble Boosted Tree (84.4%) and Logistic Regression (73.4%) provide the highest accuracy for binary classification, while Ensemble Bagged Tree (75.4%) demonstrates better accuracy for multiclassification. The findings from this research are expected to help clinicians in making an informed decision regarding the early diagnosis of Alzheimer's from the cerebrospinal fluid biomarkers alone, monitoring of the disease progression, and implementation of appropriate intervention measures.

翻译：阿尔茨海默病的早期诊断面临挑战，因为现有方法无法在患者临床前阶段（该阶段可能持续至临床症状出现前十年）识别患者。多项研究表明，脑脊液生物标志物——β-淀粉样蛋白1-42、总tau蛋白和磷酸化tau蛋白——在阿尔茨海默病分期的早期诊断中具有潜力。本研究基于脑脊液生物标志物水平单独运用机器学习模型对阿尔茨海默病不同分期进行分类。我们分析了来自国家阿尔茨海默病协调中心数据库的患者电子健康记录，并根据简易精神状态评分和临床痴呆评分将患者进行细分。通过统计分析和相关性分析识别阿尔茨海默病分期之间的显著差异。随后采用包括K近邻、集成提升树、集成装袋树、支持向量机、逻辑回归和朴素贝叶斯分类器在内的机器学习分类器对阿尔茨海默病分期进行分类。结果表明，集成提升树（84.4%）和逻辑回归（73.4%）在二分类中准确率最高，而集成装袋树（75.4%）在多分类中表现出更优的准确率。本研究的发现有望帮助临床医生仅基于脑脊液生物标志物对阿尔茨海默病早期诊断做出知情决策、监测疾病进展并实施适当的干预措施。