One-year mortality prediction of patients with hepatitis in Kazakhstan based on administrative health data: A machine learning approach
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Iliyar Arupzhanov 1 , Dmitriy Syssoyev 2 , Aidar Alimbayev 3 , Gulnur Zhakhina 2 , Yesbolat Sakko 2 , Sauran Yerdessov 2 , Amin Zollanvari 1 , Abduzhappar Gaipov 2 *
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1 School of Engineering and Digital Sciences, Nazarbayev University, Astana, KAZAKHSTAN2 Department of Medicine, School of Medicine, Nazarbayev University, Astana, KAZAKHSTAN3 Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE* Corresponding Author

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.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Original Article

ELECTRON J GEN MED, Volume 21, Issue 6, December 2024, Article No: em618

https://doi.org/10.29333/ejgm/15747

Publication date: 24 Dec 2024

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