Tag: SHAP

A clinical-radiomics machine learning model can detect hypoxic-ischemic encephalopathy in term newborns whose conventional MRI scans appear visually normal, achieving ...

Malaysian researchers have built a hybrid CNN-LSTM traffic forecasting system that pairs deep learning accuracy with Formal Concept Analysis to ...

A machine learning framework classifies patients across three diabetes stages with 98.7% accuracy while explaining exactly which clinical variables drove ...

A new study combines Double Machine Learning, explainable AI, and blockchain-based data governance to build a macroeconomic forecasting framework that ...

A new study tests whether trade, governance and environmental tax indicators can classify national CO2 emission levels, and finds that ...

Interpretable machine learning models predict 7-, 14-, and 28-day mortality in sepsis-associated liver injury, outperforming modified SOFA and MELD-Na in ...

An interpretable machine learning model trained on European injury surveillance data can identify emergency department patients whose records show documented ...

Researchers trained machine learning models on minute-by-minute facial, audio, and linguistic features from real speed dating conversations, finding that facial ...

Researchers have built DepressNet, an interpretable stacking-ensemble machine learning framework that stratifies depression severity into five levels from DASS-42 questionnaire ...

A research team proposes a framework of explainable, uncertainty-aware prediction, biologically constrained generative design and iterative design–test–learn cycles to make ...