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

from huggingface_hub import hf_hub_download
import joblib
path = hf_hub_download("anastasiyayudo/HFrEF", "model_20260925_130635.joblib")
model = joblib.load(path)