SAGE open medicine · 2026
Association of lifestyle and demographic variables with hepatitis risk: Evidence from machine learning and cross-sectional study.
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Tang Yuhang, Chen Wei
Аннотация
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.
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.