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Predicting short birth intervals in Bangladesh using stacked machine learning and SHAP explainability: evidence from
Mostakim Mia1, Shanti Akter1, Takyeatun Nesa Sraboni1
1Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh.
Reproductive Health
|May 29, 2026
Summary
Short birth intervals (SBI) are linked to poor maternal and infant health. This study identified that lower female education, higher parity, and not intending to use contraception are key predictors of SBI in Bangladesh.
Area of Science:
- Public Health
- Reproductive Health
- Machine Learning in Healthcare
Background:
- Short birth interval (SBI) is a significant public health issue linked to adverse maternal and infant outcomes.
- Identifying predictors of SBI is crucial for effective family planning and maternal health interventions.
Purpose of the Study:
- To predict short birth intervals (SBI) using stacked machine learning.
- To identify the most influential predictors of SBI through SHAP explainability.
Main Methods:
- Utilized data from the Bangladesh Demographic and Health Survey (BDHS), 2022 (n=11,872).
- Employed stacked machine learning models and SHAP explainability to predict SBI and identify key predictors.
- Addressed class imbalance using class weighting and predictor importance via Boruta and LASSO methods.
Main Results:
- The stacking ensemble model demonstrated the highest predictive performance (Accuracy: 65.8%, AUC: ~0.667).
- Key predictors identified by SHAP analysis include lower female education, higher parity, and عدم intention to use contraception.
Conclusions:
- Interventions should prioritize enhancing female education and contraceptive access to reduce SBI.
- SHAP explainability can inform data-driven reproductive health strategies for policymakers in Bangladesh.
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