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This study develops an XGBoost classifier using SHAP explainability on eight clinical biomarkers from the ADNI dataset to achieve three-class Alzheimer's disease detection (normal cognition, MCI, AD), reaching a macro AUC of 0.982 and Cohen's kappa of 0.909 on the held-out test set. SHAP analysis identifies CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drive AD classification.
Introduces TreeText-CTS, a method that converts irregular EHR trajectories into compact, source-traceable tree-path evidence units without patient-level summarization. Achieves state-of-the-art AUROC and AUPRC among text-based EHR time-series interfaces on three clinical benchmarks.
This paper studies membership inference attacks (MIA) on fine-tuned masked diffusion language models (MDLMs). It proposes a white-box attack using a 46-dimensional feature vector from the model's reconstruction loss at varying masking ratios, achieving high AUC scores and showing MDLMs are more vulnerable than previously thought.
This study applies XGBoost and SHAP analysis to CDC data to identify social determinants driving fentanyl overdose mortality in US counties, highlighting 'silent risk' areas and treatment deserts for early intervention.
A research paper presenting a dataset and XGBoost-based model for sentiment analysis of German Sign Language (DGS) fairy tales using facial and body motion features extracted via MediaPipe, achieving 63.1% balanced accuracy and demonstrating the importance of both facial and body movements for sentiment communication in sign language.