Generative Adversarial Imputation Networks (GAIN) to Handle Missing Clinical Data for IVF Success Prediction
Bibliographic record
Abstract
Missing data are a common challenge for machine learning (ML) approaches that provide clinical decision support, especially for complex medical outcomes such as In Vitro Fertilization (IVF) treatment prediction. Imputation methods have been studied to handle missing data, and shown to provide performance improvement of prediction models. Recently, Generative Adversarial Imputation Networks (GAIN) have been successfully applied to handle missing values in clinical data. This study implements and evaluates four imputation methods: a statistical, single imputation method and three ML-based imputation approaches, being KNN, MissForest, and GAIN. Imputation was applied to the full dataset, and the resulting complete data was then used to construct three task-specific datasets: blastocyst, pregnancy, and birth. Datasets are built based on a IVF treatment database of over 300 patients, and composed of over 50 discriminative features. Prediction models are built based on a 10-fold cross validation split and five classification algorithms: MLP, Random Forest, XGBoost, Gradient Boosting, and Bayesian Logistic. After applying imputation methods, feature value distributions remained largely consistent when compared to the distributions prior to imputation. Results suggest that GAIN imputation leads to effective convergence during model training, as both generator and discriminator losses decrease considerably. GAIN imputation outperformed all other ML-based imputation methods for the three prediction tasks. While single imputation yield F1 metrics for blastocyst, pregnancy, and birth of$0.50,0.56,0.13$, GAIN yield$0.74,0.75,0.76$, respectively for the negative class; for the positive class single imputation yield F1 metrics of$0.67,0.45,0.81$, while GAIN yield$0.76,0.51,0.85$respectively. Improvement of classification model performances after imputation suggest that a generative imputation approach might be beneficial to the task at hand.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".