---
title: Sundew Diabetes Commons
sdk: docker
colorFrom: green
colorTo: blue
pinned: true
emoji: 🌿
license: mit
---
# 🌿 Sundew Diabetes Watch — Advanced Edition

**Mission:** Deliver low-cost, energy-aware diabetes risk monitoring for everyone — with a special focus on communities across Africa.

This app demonstrates the **full capabilities of Sundew’s bio-inspired adaptive algorithms**, including:

- ✨ **PipelineRuntime** with a custom `DiabetesSignificanceModel`
- 📊 **Real-time energy tracking** with bio-inspired regeneration
- 🎯 **PI-control threshold adaptation** with live visualization
- 📈 **Bootstrap confidence intervals** for statistical validation
- 🔬 **Six-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** versus 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% — essential for battery-powered wearables  
- **Risk detection:** Correctly identifies hypoglycemia (<70 mg/dL) and hyperglycemia (>180 mg/dL)  
- **Adaptive thresholds:** PI controller dynamically adjusts from 0.10 → 0.95 based on glucose patterns

## 🚀 Quick Start

1. **Try the demo:** [Sundew Diabetes Watch](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch)  
2. **Upload data:** Use your CSV or the [sample_diabetes_data.csv](https://huggingface.co/spaces/mgbam/sundew_diabetes_watch/blob/main/sample_diabetes_data.csv)  
3. **Observe:** Real-time significance scoring, threshold adaptation, and energy tracking  
4. **Experiment:** Tweak Energy Pressure, Gate Temperature, and presets

## 🛠️ How It Works

1. **Upload CGM data** with columns: `timestamp, glucose_mgdl, carbs_g, insulin_units, steps, hr`  
2. **Custom significance model** computes a multi-factor diabetes risk score  
3. **Sundew gating** decides when to run the heavy ensemble model  
4. **PI control** auto-adjusts thresholds to maintain target activation  
5. **Energy management** uses bio-inspired regeneration and realistic costs  
6. **Statistical validation** via bootstrap 95% CIs (F1, Precision, Recall)  
7. **Telemetry export** (JSON) for power-measurement correlation

## 📺 Live Visualizations

- **Glucose levels:** Continuous CGM stream  
- **Significance vs. threshold:** See the PI controller adapt in real time  
- **Energy level:** Bio-inspired regeneration over time  
- **Risk components (×6):** Interpretable breakdown of the score  
- **Performance dashboard:** F1, Precision, Recall with confidence intervals  
- **Alerts:** High-risk 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 savings (lower activation)  
- **energy_saver:** Battery-optimized for edge devices

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

## 💻 Developing Locally

```bash
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: Six-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 (1,000 iterations) for 95% CI

📚 References
Sundew Algorithms

Documentation

Paper (coming soon)

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