Data-Driven Machine Learning Models for Enhanced Fetal Health Classification and Monitoring
Bibliographic record
Abstract
Fetal health classification plays a critical role in prenatal care by identifying potential risks and ensuring timely intervention. This study explores the application of multi-class classification techniques to categorize cardiotocogram (CTG) features into three distinct fetal health states: Normal, Suspect, and Pathological. CTG data, which includes fetal heart rate patterns, uterine contractions, and accelerations/decelerations, serves as a vital tool for assessing fetal well-being. Using advanced machine learning algorithms such as Logistic Regression, K Nearest Neighbors, Support Vector Classifier, Decision Trees Classifier, Random Forests Classifier, and MLP Classifier-Neural Network, we analyze and classify CTG features to predict the fetal health state with high accuracy. The results demonstrate the effectiveness of these models in distinguishing subtle variations in CTG data, contributing to improved diagnostic precision. With high accuracy for Random Forest Classifier 92%. This research highlights the potential of leveraging data-driven approaches for enhanced fetal monitoring, paving the way for better clinical outcomes and more informed decision-making in obstetric care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".