Predicting maternal risk level using machine learning models
Sulaiman Salim Al Mashrafi, Laleh Tafakori, Mali Abdollahian · 2024
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Description
Sulaiman Salim Al Mashrafi, Laleh Tafakori, and Mali Abdollahian predict pregnancy risk levels with machine learning on national data from Oman, in a 2024 study. Maternal deaths remain a global concern. Spotting high-risk pregnancies early guides care. The dataset holds four hundred two maternal deaths from 1991 to 2023. It is the first such modeling on Omani data. Ten algorithms were trained and compared. A dimension reduction step was tested on and off.
A random forest model won the comparison. With the reduction step applied, it reached about seventy-five percent accuracy. Its precision ran higher, near eighty-six percent. Tree, neighbor, and boosting methods trailed it. The authors present the model as a decision aid. It classifies risk as low or high for follow-up. They caution that more data would sharpen it further. The study shows national records can feed practical tools. Similar efforts could serve other countries.
Catalog record
| CREATOR | Sulaiman Salim Al Mashrafi, Laleh Tafakori, Mali Abdollahian |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 56 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |