Predicting low birth weight in Bangladesh using interpretable machine learning models
Samrat Kumar Dev Sharma, Md. Yusuf Hossain Ador, Md. Rukonuzzaman, Futanta Chakma, Mahmud Hossen, Jakir Hossain, Md. Kamruzzaman · 2026
Read Predicting low birth weight in Bangladesh using interpretable machine learning models by Samrat Kumar Dev Sharma, Md. Yusuf Hossain Ador, Md. Rukonuzzaman, Futanta Chakma, Mahmud Hossen, Jakir Hossain, Md. Kamruzzaman (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13393853.txt.
Description
Samrat Kumar Dev Sharma and colleagues build machine learning models to predict low birth weight in Bangladesh in a 2026 study. Low birth weight drives newborn death and lasting illness in low- and middle-income countries, and early prediction could target scarce care.
Using the 2022 Bangladesh Demographic and Health Survey, the team analyzed 3,400 mother-child pairs with survey weights and cluster-aware splitting. Seven classifiers were tested, with class imbalance handled by weighting rather than synthetic oversampling to preserve representativeness. The paper distinguishes prediction from causation and offers a tool for flagging at-risk pregnancies.
Catalog record
| CREATOR | Samrat Kumar Dev Sharma, Md. Yusuf Hossain Ador, Md. Rukonuzzaman, Futanta Chakma, Mahmud Hossen, Jakir Hossain, Md. Kamruzzaman |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 86 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |