Logo image
Back
Predicting the risk of Alzheimer's disease and Parkinson's disease using neuroimaging biomarkers and clinical characteristics
Thesis

Predicting the risk of Alzheimer's disease and Parkinson's disease using neuroimaging biomarkers and clinical characteristics

Alexandra Sahl
Master of Science (M.S.), Drexel University
Jun 2026
DOI:
https://doi.org/10.17918/00011448
pdf
Sahl_Alexandra_2026906.86 kB
PDF Embargoed Access, Embargo ends: 30 Jun 2028

Abstract

Alzheimer's disease Parkinson's disease Prospective prediction Dementia Machine Learning
Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most prevalent neurodegenerative disorders. These conditions share overlapping features, which can complicate diagnosis. Additionally, many individuals with PD eventually develop Parkinson's disease dementia (PDD). Few studies have comprehensively integrated clinical and neuroimaging data using machine learning to improve the diagnosis of neurodegenerative diseases, and even fewer have employed prospective prediction approaches. This study addresses these gaps by applying supervised machine learning models that incorporate a broad set of clinical characteristics and neuroimaging biomarkers to prospectively predict AD and PD. It also aims to identify and compare the most predictive features for each disease and to evaluate how early these conditions can be distinguished. In parallel, this research uses clinical characteristics and neurocognitive measures to prospectively predict whether individuals with Parkinson's disease without dementia (PDND) will progress to PDD or maintain their current diagnosis. It further seeks to identify features that differentiate these disease trajectories and to assess how early these outcomes can be predicted. Data from the UK Biobank (UKB) were used to support these objectives. The models developed in this study demonstrated the ability to predict future AD, PD, and PDD above chance levels. The AD/PD prediction models showed greater variability and only performed at chance once when stratified into time windows. Feature importance analyses for the best-performing AD/HC, PD/HC, and PDND/PDD models indicated that comorbidities and neurocognitive test scores were key contributors, although their relative importance varied across tasks. This study did not find evidence that predictive performance for future disease risk varied over time for any classification task. Future research should continue to integrate diverse clinical features with multimodal neuroimaging biomarkers in predictive models. Such approaches may improve the early identification of neurodegenerative diseases and support more timely intervention and treatment.

Metrics

1 Record Views

Details

Logo image