kendrickfff/Disease-Progression-Prediction

📉 Diabetes — Disease Progression Prediction (Linear Regression)

A Linear Regression model trained on the Diabetes dataset from Azure Open Datasets to predict Y (a quantitative measure of disease progression one year after baseline).

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

📊 Model Details

📝 Features (10)

📈 Performance

Best Model: Linear Regression

All Models Compared

ℹ️ Note: An R² of ~0.39 is typical for clinical datasets where disease progression depends on many unmeasured factors (genetics, lifestyle, diet). Interestingly, Linear models outperform tree-based models here due to the small sample size (442 rows), avoiding overfitting.

💻 Usage

import pickle
import numpy as np

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

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

# Predict Disease Progression
prediction = model.predict(sample)
print(f"Predicted Disease Progression (Y): {prediction[0]:.2f}")

🔍 Key Insights

• —S5 (Log of Serum Triglycerides) is the most important predictor by far (Coefficient: 65.8), indicating a strong correlation with disease progression.
• —SEX and BMI are the 2nd and 3rd most influential features.
• —Simplicity wins: Linear Regression outperforms complex ensemble models on this small dataset. Simpler models often generalize better when data is limited (n=442).
• —Stability: Cross-validation shows moderate stability (CV R² = 0.48 ± 0.05), suggesting the model is robust within its performance range.

⚖️ 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.