A Study on Machine Learning Models in Detecting Cognitive Impairments in Alzheimer's Patients Using Cerebrospinal Fluid Biomarkers.

Abstract

Several research studies have demonstrated the potential use of cerebrospinal fluid biomarkers such as amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. The levels of these biomarkers in conjunction with the dementia rating scores are used to empirically differentiate the dementia patients from normal controls. In this work, we evaluated the performance of standard machine learning classifiers using cerebrospinal fluid biomarker levels as the features to differentiate dementia patients from normal controls. We employed various types of machine learning models, that includes Discriminant, Logistic Regression, Tree, K-Nearest Neighbor, Support Vector Machine, and Naïve Bayes classifiers. The results demonstrate that these models can distinguish cognitively impaired subjects from normal controls with an accuracy ranging from 64% to 69% and an area under the curve of the receiver operating characteristics between 0.64 and 0.73. In addition, we found that the levels of 2 biomarkers, amyloid beta 1-42 and T-tau, provide a modest improvement in accuracy when distinguishing dementia patients from healthy controls.