Association of lifestyle and demographic variables with hepatitis risk: Evidence from machine learning and cross-sectional study.

BACKGROUND

Hepatitis remains a major global health concern, leading to significant morbidity and mortality worldwide. Early identification of individuals at high risk is crucial for prevention and management.

OBJECTIVE

This study aims to investigate the clinical or lifestyle variables for early detection of hepatitis risk individuals by integrated machine learning and cross-sectional study.

METHODS

We analyzed 27,387 participants from the 2023 National Health Interview Survey, randomly divided into training (n = 16,431) and validation (n = 10,956) cohorts. Least absolute shrinkage and selection operator regression was applied to identify candidate predictors, followed by univariate and multivariable logistic regression to determine independent predictors. A nomogram was developed and evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. Besides, positive predictive value, negative predictive value, and precision-recall analysis were conducted for evaluation of model efficacy and accuracy.

RESULTS

Five independent predictors were identified, including age, sex, hypertension, smoking status, and economic status of which associated with hepatitis prevalence.

CONCLUSIONS

This study is a cross-sectional, machine learning-based predictive modeling study that aims to identify key demographic and lifestyle factors associated with hepatitis risk and develop a clinically applicable risk prediction tool. Novelty, this study illustrated the association between hepatitis risk and various epidemiologic patterns, including demographic, lifestyle, and health-related factors, which facilitate the precision early-detection of hepatitis risk individuals.