Among individuals over 65 years old, Alzheimer’s Disease (AD) is the most prevalent form of dementia and remains incurable. Because AD progresses gradually over an extended period and some physical deficits appear before the onset of objective cognitive decline in both the preclinical and clinical dementia stages, it raises the need for timely diagnosis of the disease. The overarching goal of our project is to examine gait, eye movement, and electric brain activity as biomarkers for AD. A knowledge gap exists tied to AD markers; further examining the effectiveness of machine learning (ML) algorithms for predicting AD in individuals in conjunction with these measures. The integration of diverse biomarkers can enhance specificity and prove more efficacious than one modality alone. Here, we aim to analyze publicly available datasets to uncover patterns of brain dysfunction, defective eye movements, as well as gait dysfunction: 1) to characterize key features associated with AD and 2) to utilize these features in conjunction with ML techniques to detect and predict AD.

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A comprehensive Statistical analysis will be conducted to identify significant differences in feature values across AD datasets. By using ML techniques, we will identify optimal performers for disease classification based on sensitivity, prediction accuracy, and computational efficiency. Together these patterns will help us build a ML pipeline that can help us predict AD. If successful, this will be a non-invasive, low-cost, and easy way to diagnose the disease. The expected outcome of this study is to advance scientific understanding of 1) biometric features that characterize AD and 2) AD diagnosis by employing ML techniques.