Predicting early and late neonatal mortality using machine learning models in Oman
Sulaiman Salim Al Mashrafi, Laleh Tafakori, Mali Abdollahian · 2025
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Description
Sulaiman Al Mashrafi, Laleh Tafakori, and Mali Abdollahian predict newborn deaths in Oman with machine learning in a 2025 study. Newborn mortality is a global health priority, and deaths in the first week make up near half of under five deaths. A dependable early warning model could guide care and policy toward the infants at highest risk.
Ten learning models were trained to predict early deaths, in the first six days, and late deaths, up to day twenty seven. Three setups were compared, the raw local data, a resampled version that balances rare outcomes, and a cost sensitive version. Nearly three thousand de identified records were used with cross validation, and both accuracy and calibration were scored.
Catalog record
| CREATOR | Sulaiman Salim Al Mashrafi, Laleh Tafakori, Mali Abdollahian |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |