Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE
Amir Ahmad, Wasif Khan, Md Mozakkir Ansari, Mohammad M Masud, Nadirah Ghenimi, Luai A Ahmed · 2025
Read Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE by Amir Ahmad, Wasif Khan, Md Mozakkir Ansari, Mohammad M Masud, Nadirah Ghenimi, Luai A Ahmed (2025) 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-12764955.txt.
Description
Amir Ahmad and co-authors present a 2025 study on predicting very early births from health records in the United Arab Emirates. Babies born very or extremely preterm face high risks of illness and death. Standard prediction models struggle here because such births are rare in the data, and tricks to balance the classes give unstable results with so few cases.
The team instead trained one-class models that learn only from the majority group and flag anything unusual. Several such methods, and ensembles combining them, were tested on first-trimester data from Emirati women. Results were reported separately for first-time mothers and those who had given birth before. The approach aims for steadier warnings where rare-event data are thin.
Catalog record
| CREATOR | Amir Ahmad, Wasif Khan, Md Mozakkir Ansari, Mohammad M Masud, Nadirah Ghenimi, Luai A Ahmed |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |