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
The purpose of the project is to compare various predictive models to compare the effectiveness of each predictive model and to identify important features that best contribute towards predicting general cognition. Additionally, the ideal number of features were also explored for each model.
This project investigates whether there are age-dependent EEG patterns for individuals with ADHD and whether these patterns can predict neurofeedback treatment response. Using the ADHD samples from TDBrain database (n=204), we developed a random forest model to characterize age-related EEG biomarkers and assess treatment prediction across different age groups. Our model achieved AUC=0.865, identifying key EEG signatures including theta-beta ratios and frontal low-frequency patterns that vary with age and treatment response.
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