diabetes-regression-classification-fabric-model

🩺 Diabetes — Risk Classification (Logistic Regression)

A Logistic Regression classifier trained on the Diabetes dataset from Azure Open Datasets to predict Risk — specifically, whether a patient has a high risk of diabetes progression (Y > 211.5).

Built and deployed on Microsoft Fabric during Offline Workshop Training — organized by Microsoft Elevate and Dicoding.

📊 Model Details

📝 Features (10)

📈 Performance

Best Model: Logistic Regression (by ROC AUC)

All Models Compared

⚠️ Note on metrics: F1 and Precision appear moderate due to class imbalance (~22% positive class). ROC AUC of 0.79 indicates good discriminative ability. Logistic Regression achieves the best AUC due to the small dataset size (442 rows).

💻 Usage

import pickle
import numpy as np

# Load model (ensure model.pkl is in your path)
with open("model.pkl", "rb") as f:
    model = pickle.load(f)

# Input sample: [AGE, SEX, BMI, BP, S1, S2, S3, S4, S5, S6]
sample = np.array([[50, 1, 28.5, 90.0, 200, 120.5, 45.0, 4.5, 5.2, 95]])

# 1. Predict class
prediction = model.predict(sample)
print(f"Risk Prediction: {'HIGH RISK' if prediction[0] == 1 else 'LOW RISK'}")

# 2. Predict probability
proba = model.predict_proba(sample)
print(f"Risk Probability: {proba[0][1]:.2%}")

🔍 Key Insights

• S5 (log of serum triglycerides) is the strongest predictor of diabetes risk (|coef|: 0.892).
• SEX and BMI rank 2nd and 3rd — consistent with medical literature.
• All 4 models have similar ROC AUC (~0.77-0.79), indicating the feature set has a ceiling for this task.
• Logistic Regression wins on AUC; Gradient Boosting wins on F1 and Accuracy — model choice depends on the specific use case (probability vs. hard classification).

⚖️ Feature Importance

Ranked by the absolute value of coefficients:

⚠️ Intended Use

• Primary: Educational / demonstration of ML workflow on Microsoft Fabric.
• Not intended for: Clinical decision-making without further validation.

🙌 Acknowledgments

• Microsoft Elevate and Dicoding — for organizing Offline Workshop Training.
• Azure Open Datasets — for providing the Diabetes dataset.