Missingness-aware machine learning using routine laboratory data for distinguishing hepatitis from cirrhosis.

Introduction: Early identification of cirrhosis among patients with hepatitis is important for timely risk stratification and clinical management, yet conventional non-invasive scores such as the fibrosis-4 index (FIB-4) and the aspartate aminotransferase-to-platelet ratio index (APRI) may provide limited discrimination. In clinical datasets, missingness patterns may also carry predictive information, but their value for cirrhosis identification remains insufficiently investigated. Methods: We included 672 individuals, comprising 334 patients with hepatitis and 338 patients with cirrhosis, and randomly divided them into training and testing sets at in 8:2 ratio. FIB-4, APRI and multiple machine-learning models were developed using routinely available laboratory parameters; missing values were handled by median imputation, and binary missingness indicators were incorporated into selected models. Results: Machine-learning models showed better discriminatory performance than conventional scores. The Extra Trees model incorporating missingness indicators (ET + indicator) achieved the highest AUC (0.841), whereas the support vector machine model incorporating missingness indicators (SVM + indicator) achieved the highest AUPRC (0.813). By contrast, FIB-4 and APRI had AUC values of 0.537 and 0.519, respectively. Incorporating missingness indicators further improved the performance of ET, SVM and HGB models, and reducing the ET + indicator threshold from 0.50 to 0.42 increased sensitivity from 0.765 to 0.882. Discussion: Missingness-aware machine-learning models based on routine laboratory parameters may improve hepatitis-to-cirrhosis discrimination and support risk stratification in real-world screening settings.