---
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)
```