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Soulay/
diabetes-predictor

Name: diabetes-predictor
Availability: InStock

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Diabetes Disease Progression Predictor

A Gradient Boosting Regressor trained on the sklearn Diabetes dataset to predict disease progression one year after baseline (continuous target).

Results

Metric	Value
Test MAE	44.80
Test R²	0.4366
CV R² (5-fold)	0.3543

Feature Importances

Feature	Importance
bmi	0.3688
s5	0.2360
bp	0.0869
s2	0.0763
age	0.0559
s6	0.0495
s1	0.0474
s3	0.0392
s4	0.0277
sex	0.0122

Features

The model uses 10 baseline variables: age, sex, BMI, blood pressure, and 6 blood serum measurements (s1–s6).

Usage

python

import joblib
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="Soulay/diabetes-predictor", filename="model.joblib")
model = joblib.load(model_path)

# [age, sex, bmi, bp, s1, s2, s3, s4, s5, s6]  (standardised values)
prediction = model.predict([[0.05, -0.04, 0.06, 0.02, -0.01, 0.0, -0.03, 0.04, 0.02, -0.01]])
print(prediction)  # e.g. [152.3]

Training

Model trained automatically via GitHub Actions CI/CD on every push to main.