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tags:
- tabular-classification
- healthcare
- synthetic-data
license: mit
---
# HFrEF prediction model (synthetic EKG data)
XGBoost classifier that predicts **Heart Failure with Reduced Ejection Fraction (HFrEF)** from features derived from synthetic 12-lead EKG data and a synthetic lab panel.
**All training data is synthetic** - generated by our own generator. This model is a course/educational artifact and must NOT be used for any real clinical decision.
## Model version
`model_20260925_130635.joblib`
## Evaluation metrics
| Metric | Value |
|---|---|
| AUROC | 0.9349 |
| AUPRC | 0.4572 |
| Brier score | 0.0579 |
| ECE (calibration) | 0.0534 |
Confusion matrix (rows = true, cols = predicted):
```
[[342, 31], [13, 14]]
```
## Important note on the label
The label (HFrEF: ejection fraction < 40) is deliberately NOT a feature. Ground truth arrives on a delay in a separate outcomes table, so the model never sees the answer at prediction time. This is what makes the AUROC honest (high but not perfect) and the monitoring meaningful.
## How to load
```python
from huggingface_hub import hf_hub_download
import joblib
path = hf_hub_download("anastasiyayudo/HFrEF", "model_20260925_130635.joblib")
model = joblib.load(path)
```
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