Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages
arXiv:2606.09860v1 Announce Type: new Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks. Yet, population-level screening tools remain inadequate. We present Method, a machine-learning framework for NAFLD risk prediction coupling gradient-boosted decision trees with conformal prediction to yield calibrated, distribution-free coverage guarantees on individual risk estimates. It integrates a mutual-information-based stability selection procedure to identify a compact, clinically interpretable feature subse
The increasing availability of healthcare data and advancements in machine learning techniques, particularly conformal prediction, enable more reliable and interpretable AI applications in medicine.
This development offers a significant step towards more accurate and trustworthy population-level screening for widespread diseases, potentially transforming preventative healthcare and early intervention strategies.
The ability to provide distribution-free coverage guarantees with AI risk prediction models enables their safer and more effective integration into clinical practice, particularly for complex diseases like NAFLD.
- · Healthcare providers
- · Patients with chronic diseases
- · AI in healthcare developers
- · Preventative medicine sector
- · Traditional diagnostic methods
- · Companies reliant on late-stage disease treatment
Improved early detection rates for Non-alcoholic fatty liver disease (NAFLD) and better patient outcomes.
Reduced healthcare costs associated with advanced disease treatment and increased focus on preventative health strategies globally.
Broader adoption of AI with formal uncertainty quantification across various medical diagnostic and prognostic applications, setting new standards for clinical AI safety.
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Read at arXiv cs.LG