AINeutralarXiv – CS AI · 6h ago6/10
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Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages
Researchers developed a machine-learning framework combining gradient-boosted decision trees with conformal prediction to improve non-alcoholic fatty liver disease (NAFLD) risk screening. The model achieved 91.2% internal and 89.1% external validation accuracy while identifying six key metabolic biomarkers, enabling better population-level disease stratification.