EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Methodological Paper
Sep 24, 2025 33 13

Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data

Iliyar Arupzhanov 1 * ,
Aidar Alimbayev 2,
Temirlan Seyil 1,
Temirgali Aimyshev 1,
Tilektes Maulenkul 1,
Ainash Oshibayeva 3,
Abduzhappar Gaipov 1 *
1 School of Medicine, Nazarbayev University, Astana, Kazakhstan2 Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE 3 Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan * Corresponding Author
Epidemiology and Health Data Insights, 1(4), 2025, ehdi015, https://doi.org/10.63946/ehdi/17159
Publication date: Sep 24, 2025
Full Text (PDF)

ABSTRACT

Chronic diseases remain a leading cause of global mortality, underscoring the need for developing reliable models that predict mortality prediction to guide individualized treatments and optimize resource allocation. This methodological note presents a reproducible framework for predicting one-year mortality in chronic disease patients using large-scale administrative healthcare data. The approach employs retrospective cohort design, year-specific subcohorts, and a stratified 5-fold cross-validation using a broad range of machine learning models. Performance is assessed with multiple metrics, including AUC, sensitivity, specificity, and balanced accuracy, to account for class imbalance. Model interpretability is enhanced through SHapley Additive exPlanations (SHAP), enabling identification of key mortality predictors and their directional impact. The proposed framework is general and can be applied to different chronic diseases. It has already been successfully demonstrated in nationwide cohorts of patients with diabetes mellitus and chronic viral hepatitis in Kazakhstan, achieving AUC values of 0.74–0.80, comparable to international benchmarks despite relying on administrative data alone. The method is scalable and adaptable, allowing integration of laboratory and clinical data with feature selection to address high-dimensionality challenges. Its generalizability and clinical relevance, however, should be validated in practice using enriched datasets across additional chronic diseases and diverse populations.

KEYWORDS

One-Year Mortality Prediction Chronic Diseases Machine Learning SHAP Analysis

CITATION (Vancouver)

Arupzhanov I, Alimbayev A, Seyil T, Aimyshev T, Maulenkul T, Oshibayeva A, et al. Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data. Epidemiology and Health Data Insights. 2025;1(4):ehdi015. https://doi.org/10.63946/ehdi/17159
APA
Arupzhanov, I., Alimbayev, A., Seyil, T., Aimyshev, T., Maulenkul, T., Oshibayeva, A., & Gaipov, A. (2025). Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data. Epidemiology and Health Data Insights, 1(4), ehdi015. https://doi.org/10.63946/ehdi/17159
Harvard
Arupzhanov, I., Alimbayev, A., Seyil, T., Aimyshev, T., Maulenkul, T., Oshibayeva, A., and Gaipov, A. (2025). Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data. Epidemiology and Health Data Insights, 1(4), ehdi015. https://doi.org/10.63946/ehdi/17159
AMA
Arupzhanov I, Alimbayev A, Seyil T, et al. Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data. Epidemiology and Health Data Insights. 2025;1(4), ehdi015. https://doi.org/10.63946/ehdi/17159
Chicago
Arupzhanov, Iliyar, Aidar Alimbayev, Temirlan Seyil, Temirgali Aimyshev, Tilektes Maulenkul, Ainash Oshibayeva, and Abduzhappar Gaipov. "Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data". Epidemiology and Health Data Insights 2025 1 no. 4 (2025): ehdi015. https://doi.org/10.63946/ehdi/17159
MLA
Arupzhanov, Iliyar et al. "Methodological Note on Predicting One-Year Mortality for Chronic Diseases Using Administrative Data". Epidemiology and Health Data Insights, vol. 1, no. 4, 2025, ehdi015. https://doi.org/10.63946/ehdi/17159

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