Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques
Bibliographic record
Abstract
The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".