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Early diagnosis of Alzheimer's disease is a challenge because the existingmethodologies do not identify the patients in their preclinical stage, whichcan last up to a decade prior to the onset of clinical symptoms. Severalresearch studies demonstrate the potential of cerebrospinal fluid biomarkers,amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's diseasestages. In this work, we used machine learning models to classify differentstages of Alzheimer's disease based on the cerebrospinal fluid biomarker levelsalone. An electronic health record of patients from the National Alzheimer'sCoordinating Centre database was analyzed and the patients were subdividedbased on mini-mental state scores and clinical dementia ratings. Statisticaland correlation analyses were performed to identify significant differencesbetween the Alzheimer's stages. Afterward, machine learning classifiersincluding K-Nearest Neighbors, Ensemble Boosted Tree, Ensemble Bagged Tree,Support Vector Machine, Logistic Regression, and Naive Bayes classifiers wereemployed to classify the Alzheimer's disease stages. The results demonstratethat Ensemble Boosted Tree (84.4%) and Logistic Regression (73.4%) provide thehighest accuracy for binary classification, while Ensemble Bagged Tree (75.4%)demonstrates better accuracy for multiclassification. The findings from thisresearch are expected to help clinicians in making an informed decisionregarding the early diagnosis of Alzheimer's from the cerebrospinal fluidbiomarkers alone, monitoring of the disease progression, and implementation ofappropriate intervention measures.

Machine Learning Classification of Alzheimer's Disease Stages Using Cerebrospinal Fluid Biomarkers Alone