README.md

metadata

title: Sundew Diabetes Watch - ADVANCED
sdk: docker
colorFrom: green
colorTo: blue
pinned: true
emoji: 🌿
license: mit

🌿 Sundew Diabetes Watch — ADVANCED EDITION

Mission: Low-cost, energy-aware diabetes risk monitoring for everyone — especially communities across Africa.

This app showcases the full power of Sundew's bio-inspired adaptive algorithms with:

• ✨ PipelineRuntime with custom DiabetesSignificanceModel
• 📊 Real-time energy tracking with bio-inspired regeneration
• 🎯 PI control threshold adaptation with live visualization
• 📈 Bootstrap confidence intervals for statistical validation
• 🔬 6-factor diabetes risk computation (glycemic deviation, velocity, IOB, COB, activity, variability)
• 🤖 Ensemble model (LogReg + RandomForest + GBM)
• 💾 Telemetry export for hardware validation workflows
• 🌍 89.8% energy savings vs always-on inference (validated on real CGM data)

✅ Proven Results

Tested on 216 continuous glucose monitoring events (18 hours):

• Activation Rate: 10.2% (22/216 events) — intelligently selective
• Energy Savings: 89.8% — critical for battery-powered wearables
• Risk Detection: Correctly identifies hypoglycemia (<70 mg/dL) and hyperglycemia (>180 mg/dL)
• Adaptive Thresholds: PI controller dynamically adjusts from 0.1 to 0.95 based on glucose patterns

🚀 Quick Start

1. Try the demo: Visit Sundew Diabetes Watch
2. Upload sample data: Download sample_diabetes_data.csv (or use the synthetic example)
3. Watch it work: See real-time significance scoring, threshold adaptation, and energy tracking
4. Experiment: Adjust Energy Pressure, Gate Temperature, and preset configurations

How It Works

1. Upload CGM Data: CSV with timestamp, glucose_mgdl, carbs_g, insulin_units, steps, hr
2. Custom Significance Model: Computes multi-factor diabetes risk score
3. Sundew Gating: Adaptively decides when to run heavy ensemble model
4. PI Control: Threshold auto-adjusts to maintain target activation rate
5. Energy Management: Bio-inspired regeneration + realistic consumption costs
6. Statistical Validation: Bootstrap 95% CI for F1, Precision, Recall
7. Telemetry Export: JSON download for hardware power measurement correlation

Live Visualizations

• Glucose Levels: Real-time CGM data
• Significance vs Threshold: Watch the PI controller adapt!
• Energy Level: Bio-inspired regeneration visualization
• 6-Factor Risk Components: Interpretable diabetes scoring breakdown
• Performance Dashboard: F1, Precision, Recall with confidence intervals
• Alerts: High-risk event notifications

Configuration Presets

• custom_health_hd82: Healthcare-optimized (82% energy savings, 0.196 recall)
• tuned_v2: Balanced general-purpose baseline
• auto_tuned: Dataset-adaptive configuration
• conservative: Maximum energy savings (low activation)
• energy_saver: Battery-optimized for edge devices

Disclaimer: Research prototype. Not medical advice. Not FDA/CE approved.

Developing Locally

python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt
streamlit run app_advanced.py

Technical Details

• Algorithm: Sundew bio-inspired adaptive gating
• Model: Ensemble (LogReg + RandomForest + GBM)
• Risk Factors: 6-component diabetes-specific significance model
• Control: PI threshold adaptation with energy pressure feedback
• Energy Model: Random regeneration (1.0–3.0 per tick) + realistic costs
• Validation: Bootstrap resampling (1000 iterations) for 95% CI

References

• Sundew Algorithms
• Documentation
• Paper (coming soon)

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