Predicting Tau Burden and Tau Positivity from Multimodal Neuroimaging and Clinical Data in ADNI
This project is a complete and reproducible ML pipeline using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to: Predict tau PET SUVR as a continuous outcome, predict tau positivity as a binary outcome (classify participants as tau-positive or tau-negative), and identify which predictors drive model performance using SHAP explainability