We developed interpretable machine learning models to predict five-year β-Amyloid (Aβ) plaque progression in individuals at early risk for Alzheimer’s Disease (AD). Focusing on subjects with initially low Aβ burden (Centiloid < 24) from the ADNI cohort, we integrated demographic information, APOE genotype, cognitive assessments, and PET-derived features—including regional SUVRs, brain volumes and their change rates. Using support vector machine, random forest, and multilayer perceptron models, we achieved strong performance (F1-scores up to 0.866), with consistent generalization to the independent OASIS-3 dataset.
To ensure transparency, we applied SHAP (Shapley Additive Explanations) and found that changes in prefrontal SUVR, regional volumes, and SUVRs in parietal and posterior cingulate regions were among the most influential predictors. Our approach offers an interpretable and practical tool for identifying individuals at high risk of Aβ buildup, supporting early diagnosis and timely intervention in AD.
A: Prognosis pipeline for β-Amyloid plaque burden.
B: ML model performance (weighted F1-score).
C: PET feature contributions based on averaged normalized SHAP values from MLP outputs.