Mar 5, 2026 · Artificial Intelligence

ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Using a dataset of 11,647 hepatocellular carcinoma patients, a French research team combined ensemble learning, SHAP explainability, UMAP dimensionality reduction and K‑medoids clustering to build an interpretable model that outperforms traditional scores in predicting three‑month wait‑list mortality and defines seven clinically distinct risk sub‑groups.

Hepatocellular CarcinomaK-MedoidsLiver Transplantation

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ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Data STUDIO

Sep 9, 2025 · Artificial Intelligence

10 Hidden Sklearn Features That Boost Your ML Pipelines

This article walks through ten lesser‑known Scikit‑learn utilities—including FunctionTransformer, custom estimators, TransformedTargetRegressor, HTML estimator visualisation, QuadraticDiscriminantAnalysis, Voting and Stacking ensembles, LocalOutlierFactor with UMAP, QuantileTransformer, and a PCA‑tSNE/UMAP workflow—showing concrete code examples, performance numbers and practical tips for more efficient and robust machine‑learning pipelines.

FunctionTransformerLocalOutlierFactorMachine Learning

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10 Hidden Sklearn Features That Boost Your ML Pipelines