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Original Articles
- Development of Machine Learning Models to Predict Health Insurance Claim Costs Among Older Indonesians: A Retrospective Predictive Modeling Study
- Yeni Mahwati, Dhihram Tenrisau, Syarif Rahman Hasibuan, Bhirau Wilaksono, Yeni Indriyani, Andi Afdal Abdullah, Halik Malik, Andi Alfian Zainuddin
- J Prev Med Public Health. 2026;59(2):132-142. Published online January 6, 2026
- DOI: https://doi.org/10.3961/jpmph.25.350
- 2,526 View
- 297 Download
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Abstract
Summary
PDF
Supplementary Material - Objectives
The objective of this study was to develop machine learning models to predict health insurance claim costs among older adults in Indonesia.
Methods
This study utilized secondary data from the Indonesian National Health Insurance program (Jaminan Kesehatan Nasional [JKN]) spanning 2017 to 2023. Three modeling techniques—linear regression, random forest, and XGBoost—were employed to predict individual claim costs. Model performance was assessed using the root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Additionally, variable importance analysis was conducted to identify key predictors.
Results
XGBoost with 500 boosting rounds yielded the best performance, with an RMSE of 11 360 283, an R2 of 0.81, and an MAE of 4 485 917, outperforming both linear regression (RMSE, 13 710 035; R2=0.72) and random forest (RMSE, 12 434 238; R2=0.78). Notably, outpatient care was identified as the most consistent predictor across all models. Other significant predictors included length of stay (LOS), diagnosis type (International Classification of Diseases, 10th revision chapter), facility type, facility classification, and severity of illness, particularly for moderate cases. Although LOS and diagnosis type were important predictors, these findings should be interpreted in the context of Indonesia’s fixed Indonesian Case-Based Groups payment system.
Conclusions
XGBoost provides reliable predictions of claim costs among older adults, capturing clinical, utilization, and structural drivers. These findings can inform targeted interventions, improve chronic disease management, optimize the referral system, and support integration of predictive tools into JKN to enhance responsiveness and promote sustainable, equitable financing. -
SummaryKey Message
Machine learning models, particularly XGBoost, demonstrated superior performance in predicting healthcare costs among older adults in Indonesia. Nonlinear relationships between outpatient visits, severity, and diagnoses highlight the limitations of conventional linear approaches. These findings support the integration of advanced predictive models into national health insurance systems to improve cost management and resource allocation.- The Determinants of Undiagnosed Hypertension Among Indonesian Adults: A Cross-sectional Study Based on the 2014-2015 Indonesia Family Life Survey
- Yeni Mahwati, Dieta Nurrika, Kamaluddin Latief
- J Prev Med Public Health. 2022;55(1):60-67. Published online January 6, 2022
- DOI: https://doi.org/10.3961/jpmph.21.500
- 9,409 View
- 365 Download
- 15 Web of Science
- 18 Crossref
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Abstract
PDFSupplementary Material
- Objectives
This study investigated the determinants of undiagnosed hypertension among Indonesian adults.
Methods
This study involved an analysis of secondary data from the 2014 Indonesia Family Life Survey (IFLS) on 5914 Indonesian adults (≥40 years). The determinant variables examined in this cross-sectional study were education level, monthly per capita expenditures (PCE), whether the participant experienced headaches in the morning, and other general health variables. The outcome variable was undiagnosed hypertension, which was defined as participants with hypertension who had not received a hypertension diagnosis from a health professional and had never been prescribed medication for treating hypertension. The data were analyzed using logistic regression.
Results
A total of 3322 participants (56.2%) were found to have undiagnosed hypertension. The odds ratios (ORs) and 95% confidence intervals (CIs) of undiagnosed hypertension were significantly higher among those who completed primary school or lower (OR, 1.60; 95% CI, 1.29 to 1.98), had low monthly PCE (OR, 1.28; 95% CI, 1.13 to 1.43), did not report experiencing headaches in the morning (OR, 1.97; 95% CI, 1.76 to 2.21), and reported a general health status of healthy (OR, 2.05; 95% CI, 1.82 to 2.30) than those who had a higher education level, had high monthly PCE, experienced headaches in the morning, and were unhealthy.
Conclusions
Education level, monthly PCE, the experience of headaches in the morning, and general health status were associated with undiagnosed hypertension. The monitoring system for detecting undiagnosed hypertension cases must be strengthened. Health promotion is also necessary to reduce the prevalence of undiagnosed hypertension. -
Summary
Citations
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