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README.md

@@ -1,3 +1,45 @@
----
-license: unknown
----
+---
+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_20260912_135646.joblib`
+
+## Evaluation metrics
+
+| Metric | Value |
+|---|---|
+| AUROC | 0.9720 |
+| AUPRC | 0.6795 |
+| Brier score | 0.0458 |
+| ECE (calibration) | 0.0464 |
+
+Confusion matrix (rows = true, cols = predicted):
+
+```
+[[185, 2], [10, 3]]
+```
+
+## 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_20260912_135646.joblib")
+model = joblib.load(path)
+```