A Machine Learning Approach for Differential Diagnosis and Prognostic Prediction in Alzheimer's Disease

Abstract

This study presents a machine learning-driven approach designed to address the intricate challenges of Alzheimer's disease (AD) diagnosis and prognosis. Leveraging a diverse dataset encompassing neuroimaging scans, clinical assessments, and demographic data from AD patients and healthy controls, our model integrates multimodal neuroimaging features from structural MRI, functional MRI, and PET scans alongside demographic and clinical variables. Our aim is two-fold: first, to construct a robust differential diagnosis model adept at accurately discerning various AD stages and distinguishing AD patients from healthy individuals; secondly, to establish a prognostic prediction model capable of estimating disease progression and forecasting clinical outcomes in AD patients. Our results demonstrate the model's efficacy, showcasing high accuracy, sensitivity, and specificity in classifying different AD stages and discriminating between AD patients and healthy controls. Additionally, the prognostic prediction model shows promise in forecasting disease progression and estimating future clinical outcomes for individual patients. This machine learning framework not only advances differential diagnosis in AD but also lays the groundwork for personalized prognostic predictions, potentially aiding clinicians in early diagnosis, patient stratification, and personalized treatment planning.

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