One-year mortality prediction of patients with hepatitis in Kazakhstan based on administrative health data: A machine learning approach

Iliyar Arupzhanov, Dmitriy Syssoyev, Aidar Alimbayev, Gulnur Zhakhina, Yesbolat Sakko, Sauran Yerdessov, Amin Zollanvari, Abduzhappar Gaipov

2024

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.29333/ejgm/15747
Data confidence 85 high
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Abstract

Background and objective: Hepatitis B virus (HBV) and hepatitis C virus (HCV) are major contributors to chronic viral hepatitis (CVH), leading to significant global health mortality. This study aims to predict the one-year mortality in patients with CVH using their demographics and health records. Methods: Clinical data from 82,700 CVH patients diagnosed with HBV or HCV between January 2014 and December 2019 was analyzed. We developed a machine learning (ML) platform based on six broad categories including linear, nearest neighbors, discriminant analysis, support vector machine, naïve Bayes, and ensemble (gradient boosting, AdaBoost, and random forest) models to predict the one-year mortality. Feature importance analysis was performed by computing SHapley Additive exPlanations (SHAP). Results: The models achieved an area under the curve between 0.74 and 0.8 on independent test sets. Key predictors of mortality were age, sex, hepatitis type, and ethnicity. Conclusion: ML with administrative health data can be utilized to accurately predict one-year mortality in CVH patients. Future integration with detailed laboratory and medical history data could further enhance model performance.