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.