Tag: SHAP interpretability

A simplified 11-variable random forest model predicted in-hospital mortality among ICU patients surviving 24 hours after traumatic brain injury with ...

A hybrid stacking ensemble of XGBoost, LightGBM and CatBoost with domain-informed features predicts concrete compressive strength with an R² of ...

An interpretable random forest model trained on early emergency resuscitation data accurately predicted extubation failure in mechanically ventilated patients with ...

An explainable XGBoost model using eight routine clinical parameters predicted sepsis-associated acute kidney injury in 3,437 children across five Chinese ...

A four-habitat MRI radiomics model combined with clinical variables predicted lymph node metastasis in early-stage cervical cancer with a test ...

Triaxial tests and an interpretable machine learning framework show that matrix cementation reverses whether soft rock blocks strengthen or weaken ...

A hybrid vision transformer-CNN model detected epileptic seizures in pediatric EEG recordings with 99.8 percent average sensitivity while quantitatively explaining ...

Researchers trained an interpretable XGBoost model on biomechanical data from 204 male athletes to predict peak ACL force during side-cutting ...

A stacked ensemble learning framework using low-cost inertial sensors outperformed traditional trapezoidal integration in predicting soil slide displacement across three ...

A rolling-aware machine learning system warns of tearing modes in the EAST tokamak about 1.5 seconds ahead while cutting false ...