Auto machine learning to predict pregnancy after fresh embryo transfer following in vitro fertilization
Bibliographic record
Abstract
Introduction: The use of human reproduction techniques (ART) to obtain pregnancy are increasing. However pregnancy rates after ART remain as low as around 30%. The use of machine Learning (ML) is increasing in medicine and prediction models are helpful to preview the outcome of in vitro fertilization(IVF) cycles. Methods: Data from IVF cycles with fresh embryo transfer between January 2018 and December 2021 were collected. The Auto Machine Learning (Auto ML) PyCaret was used to construct the model and predict the clinical pregnancy rate. Results: Among 14 ML algorithms, Ridge Classification (RC) has the best accuracy(57,69%). Transfer in day 5 was the most important feature related to the outcome. Conclusion: Despite the low accuracy as a result of a small sample, familiarization with ML models, as well as awareness of the importance of data collection should be part of daily activities of physicians and healthcare professionals in the field of ART.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".