DOI: https://doi.org/10.14710/vol15iss4pp%25p

Improving Low Birth Weight Prediction in Indonesia Through Hybrid Resampling and XGBoost Class Weighting with SHAP Interpretation

*Bramono Triprastowo  -  Master Program of Information Systems, Postgraduate School, Universitas Diponegoro, Indonesia, Indonesia
Adi Wibowo  -  Department of Informatics, Faculty of Science and Mathematics, Universitas Diponegoro, Indonesia, Indonesia
Helmie Arif Wibawa  -  Department of Informatics, Faculty of Science and Mathematics, Universitas Diponegoro, Indonesia, Indonesia
Open Access Copyright (c) 2026 Jurnal Sistem Informasi Bisnis

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Abstract

Newborns are classified as having low birth weight (LBW) if they weigh less than 2,500 grams (5.5 pounds), which poses risks for stunted growth, developmental delays, and long-term health issues such as cardiovascular disease. This study develops an XGBoost based machine learning model for low birth weight classification using the 2017 Indonesia Demographic and Health Survey (IDHS). To address severe class imbalance, various resampling techniques including undersampling, oversampling, hybrid methods, and algorithm level class reweighting are applied, along with a modified random oversampling method (M-ROS). SHAP and XGBoost’s built in feature importance are then used to identify key predictors of low birth weight in the Indonesian context. A total of 16,340 records with 42 preprocessed features were used in this study. The dataset was split into 70 percent for training and 30 percent for testing, and stratified k-fold cross-validation with random search was employed to identify the optimal hyperparameters. Among the resampling strategies evaluated, random oversampling, M ROS, ENN with class reweighting, and class reweighting without any resampling were able to improve recall. Of these approaches, class reweighting without resampling achieved the highest recall at 36.63 percent, while ENN combined with class reweighting produced the highest accuracy at 84.72 percent. Key predictors influencing the classification of low birth weight included the number of antenatal care visits, multiple pregnancy, and place of delivery. The findings demonstrate that machine learning can be effectively utilized to predict low birth weight and to identify influential contributing factors.

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Keywords: Low Birth Weight; XGBoost; Machine Learning Classification, Imbalanced Data; Resampling; Feature Importance

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Section: Research Articles
Language : EN
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  1. Blencowe, H., Krasevec, J., de Onis, M., Black, R. E., An, X., Stevens, G. A., Borghi, E., Hayashi, C., Estevez, D., Cegolon, L., Shiekh, S., Ponce Hardy, V., Lawn, J. E., & Cousens, S. (2019). National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. The Lancet Global Health, 7(7), e849–e860. https://doi.org/10.1016/S2214-109X(18)30565-5
  2. Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE : Synthetic Minority Over-sampling Technique. 16, 321–357
  3. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794. https://doi.org/10.1145/2939672.2939785
  4. Chen, W., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. P. (2024). A survey on imbalanced learning: latest research, applications and future directions. Artificial Intelligence Review, 57(6). https://doi.org/10.1007/s10462-024-10759-6
  5. He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the International Joint Conference on Neural Networks, March, 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969
  6. Kemenkes RI. (2018). Indonesia Demographic and Health Survey 2017. 588. https://dhsprogram.com/pubs/pdf/FR342/FR342.pdf
  7. Khan, W., Zaki, N., Masud, M. M., Ahmad, A., Ali, L., Ali, N., & Ahmed, L. A. (2022). Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms. Scientific Reports, 12(1), 1–12. https://doi.org/10.1038/s41598-022-14393-6
  8. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-Decem(Section 2), 4766–4775
  9. Mia, R., Khanam, S., Mahjabeen, A., Ovy, N. H., Ghimire, D., Park, M. J., Begum, M. I. A., & Hosen, A. S. M. S. (2024). Exploring Machine Learning for Predicting Cerebral Stroke: A Study in Discovery. Electronics (Switzerland), 13(4), 1–17. https://doi.org/10.3390/electronics13040686
  10. Ngusie, H. S., Mengiste, S. A., Zemariam, A. B., Molla, B., Tesfa, G. A., Seboka, B. T., Alene, T. D., & Sun, J. (2024). Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques. BMC Public Health, 24(1). https://doi.org/10.1186/s12889-024-19566-8
  11. Ranjbar, A., Montazeri, F., Farashah, M. V., Mehrnoush, V., Darsareh, F., & Roozbeh, N. (2023). Machine learning-based approach for predicting low birth weight. BMC Pregnancy and Childbirth, 23(1), 1–7. https://doi.org/10.1186/s12884-023-06128-w
  12. Ren, Y., Wu, D., Tong, Y., López-DeFede, A., & Gareau, S. (2023). Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies. Journal of Medical Internet Research, 25(5), 1–56. https://doi.org/10.2196/44081
  13. WHO, & UNICEF. (2004). Low Birthweight - Country, Regional And Global Estimates. In Man. https://doi.org/10.2307/2800038
  14. Yoon, S. J., Kim, D., Park, S. H., Han, J. H., Lim, J., Shin, J. E., Eun, H. S., Lee, S. M., & Park, M. S. (2023). Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model. Diagnostics, 13(24), 3627. https://doi.org/10.3390/diagnostics13243627

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