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


Arupzhanov I. Syssoyev D. Alimbayev A. Zhakhina G. Sakko Y. Yerdessov S. Zollanvari A. Gaipov A.
December 2024Modestum LTD

Electronic Journal of General Medicine
2024#21Issue 6

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.

artificial intelligence , chronic viral hepatitis , machine learning , mortality prediction , SHAP analysis

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School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Department of Medicine, School of Medicine, Nazarbayev University, Astana, Kazakhstan
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

School of Engineering and Digital Sciences
Department of Medicine
Mohamed bin Zayed University of Artificial Intelligence

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