TabPFN reaches AUC 0.892 for 3-year MCI-to-AD conversion on TADPOLE data and holds performance at N=50 training samples where XGBoost, Random Forest, LightGBM, and logistic regression drop.
Machine learning models show promise for predicting disease progression, yet their development faces a fundamental challenge: limited high-quality train- ing data
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Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings
TabPFN reaches AUC 0.892 for 3-year MCI-to-AD conversion on TADPOLE data and holds performance at N=50 training samples where XGBoost, Random Forest, LightGBM, and logistic regression drop.