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HeartSync — Multi-Modal AI for Cardiovascular Disease Detection

Hack4Health AI Challenge

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Inspiration

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, responsible for over 17.9 million deaths annually.

Despite major advancements in healthcare, most diagnostic systems still rely on single-source data — either clinical reports, ECG readings, or lifestyle indicators — which often miss the full physiological picture.

We asked:

What if we could unify clinical data, ECG signals, and real-world wearable monitoring into one intelligent early-detection system?

HeartSync was created to answer that challenge.

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What it does

HeartSync is a multi-modal AI cardiovascular detection platform that integrates:

• 🧬 Population health records
  
• 🏥 Clinical diagnostic features
  
• 📈 Raw and labeled ECG signals
  
• ⌚ Wearable heart-rate sensor data
  

The system:

• Extracts physiological features
  
• Trains ML models across data modalities
  
• Predicts cardiovascular disease risk
  
• Produces interpretable health insights
  

Model performance exceeded 0.80 AUC across datasets.

Inline metric example: ( AUC > 0.80 )

Displayed equation:

$$ AUC = \int_0^1 TPR(FPR)\,d(FPR) $$

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How we built it

Data Fusion

Dataset	Size	Why We Used It
Cardio Base	70K	Large-scale risk factor analysis
Heart Clinical	918	High-quality diagnostic features
PTB-XL ECG	21,837	Expert-labeled ECG patterns
Raw ECG Signals	500	Signal-level feature engineering
Fitbit Wearables	2.5M HR points	Real-world continuous monitoring
SCP Codes	73	Clinical interpretability

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Modeling Pipeline

• Signal processing on ECG waveforms
  
• Feature extraction & normalization
  

• Machine learning classifiers:

  • Logistic Regression
    
  • Random Forest
    
  • Gradient Boosting
    
  • Neural Networks
    

• Cross-validation and AUC benchmarking

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Challenges we ran into

• Data format heterogeneity
  
• Noisy ECG and wearable signals
  
• Class imbalance in medical data
  
• Feature alignment across modalities
  

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Accomplishments that we're proud of

• Built a full multi-modal AI healthcare system
  
• Achieved strong predictive performance
  
• Integrated wearable sensor analytics
  
• Developed interpretable medical risk models
  

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What we learned

• Multi-modal AI outperforms single-source models
  
• Biomedical signal processing is critical
  
• Interpretability is essential in healthcare
  
• Real-world data improves preventive detection
  

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What’s next for HeartSync

• Real-time wearable integration
  
• Deep learning on ECG streams
  
• Mobile health dashboard
  
• Clinical deployment trials
  
• Preventive risk alert systems
  

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HeartSync’s mission:
Unifying AI and healthcare data to prevent cardiovascular disease before it strikes.

Built With

• collab
• python