Racial Disparities in Comorbidity Patterns of Early-Onset Liver Cancer: A Machine Learning Analysis.

ABSTRACT: IntroductionThe incidence of early-onset liver cancer (EOLC) has been increasing in many countries, yet evidence on its etiology remains limited, particularly outside the Asian population. This case-control study explores the comorbidity patterns of EOLC and develops race/ethnicity-specific machine learning (ML) models to predict liver cancer risk.MethodsWe included patients diagnosed with primary liver cancer between ages 18 and 49 from the University of California Health Data Warehouse, matching each patient with five controls. ML classification methods, including decision trees, random forests, logistic regression, XGBoost, and LightGBM, were used to assess liver cancer risk based on demographics and comorbidities. Model performance was evaluated using F1 scores, and SHapley Additive ex

SUBMITTER: Ma B 

PROVIDER: S-EPMC12317173 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature