  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.
