# ๐ฅ ExplainableAI Heart Disease Predictor
**A clinical-grade AI system that predicts heart disease risk with 94.1% accuracy โ and explains every decision in plain clinical language.**
[](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor)
[](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor)
[](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor)
[](LICENSE)
[](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor)
[๐ Try Live Demo](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor) ยท [๐ Docs](./docs) ยท [๐ Report Bug](https://github.com/Ariyan-Pro/ExplainableAI-HeartDisease/issues) ยท [๐ก Request Feature](https://github.com/Ariyan-Pro/ExplainableAI-HeartDisease/issues)
---
## ๐ฉบ Why This Project?
Clinical AI tools today often behave as black boxes โ a model outputs a risk score, but clinicians have no way to understand *why*. This erodes trust, prevents adoption, and creates liability in high-stakes environments.
**ExplainableAI Heart Disease Predictor** bridges this gap: it combines state-of-the-art predictive performance (94.1% accuracy, 0.967 AUC) with full SHAP + LIME explainability, so every prediction comes with a human-readable clinical rationale โ not just a number.
---
## ๐ฏ Clinical Impact
| Metric | Clinical Standard | Our Performance | Improvement |
|:-------|:-----------------|:----------------|:------------|
| **Accuracy** | 85โ90% | **94.1%** | **+4.1% to +9.1%** |
| **AUC Score** | 0.85โ0.92 | **0.967** | **+0.047 to +0.117** |
| **Explainability** | Limited | **100% Coverage** | **Full Transparency** |
| **Federated Learning** | Rare | **85.9% FL Accuracy** | **Privacy-Preserving** |
| **Response Latency** | Varies | **< 100ms** | **Real-time Clinical Use** |
---
## โจ Features
- **๐ฌ Dual Explainability (SHAP + LIME)** โ Every prediction generates both global and local explanations, giving clinicians feature-level insight into risk factors driving each diagnosis.
- **๐ 94.1% Predictive Accuracy** โ Validated performance exceeding typical clinical decision-support benchmarks, with 0.967 ROC-AUC on held-out test data.
- **๐ Federated Learning Support** โ Train across distributed clinical sites without sharing raw patient data โ GDPR/HIPAA-aligned by design (85.9% FL accuracy).
- **โก Production FastAPI Backend** โ REST API with sub-100ms inference latency, ready for EHR system integration and clinical workflow embedding.
- **๐ Enterprise MLOps via MLflow** โ Full experiment tracking, model versioning, artifact logging, and reproducible training pipelines.
- **๐ฅ๏ธ Interactive Gradio Dashboard** โ A professional, no-code UI for clinicians to input patient data and receive annotated risk assessments in real time.
- **โ๏ธ Cloud-Ready Deployment** โ One-command deploy to Hugging Face Spaces, Streamlit Cloud, or Render โ no DevOps expertise required.
---
## ๐ Performance Proof
**Validation Results โ 94.1% Accuracy**
---
## ๐ Live Demo Interface
**Professional Medical AI Dashboard**
[](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor)
*Click the image above to launch the live demo on Hugging Face Spaces*
---
## โ๏ธ Quick Start
### Prerequisites
- Python 3.9+
- pip or conda
### Installation
```bash
# 1. Clone the repository
git clone https://github.com/Ariyan-Pro/ExplainableAI-HeartDisease.git
cd ExplainableAI-HeartDisease
# 2. Install dependencies
pip install -r requirements.txt
# 3. Launch the dashboard
python dashboard/app.py
```
The Gradio interface will be available at `http://localhost:7860`
### API Server (FastAPI)
```bash
# Start the REST API
uvicorn healthcare_model.api:app --reload --port 8000
```
API docs auto-generated at `http://localhost:8000/docs`
### Quick Prediction (Python)
```python
from healthcare_model import HeartDiseasePredictor
predictor = HeartDiseasePredictor()
patient = {
"age": 55,
"sex": 1,
"cp": 2, # Chest pain type
"trestbps": 140, # Resting blood pressure
"chol": 250, # Serum cholesterol
"fbs": 0,
"restecg": 1,
"thalach": 150, # Max heart rate achieved
"exang": 0,
"oldpeak": 1.5,
"slope": 2,
"ca": 0,
"thal": 2
}
result = predictor.predict(patient)
print(result["risk_score"]) # โ 0.82 (high risk)
print(result["explanation"]) # โ SHAP + LIME feature breakdown
```
---
## ๐๏ธ System Architecture
**Enterprise-Grade Four-Layer Architecture**
The system is organized into four layers:
| Layer | Components | Role |
|:------|:-----------|:-----|
| **Interface** | Gradio Dashboard, FastAPI REST | User interaction & external integrations |
| **Explainability** | SHAP Engine, LIME Engine | Post-hoc explanation generation |
| **ML Core** | Trained Classifier, Federated Client | Prediction & privacy-preserving training |
| **MLOps** | MLflow Tracking, Artifact Store | Experiment management & reproducibility |
---
## ๐ฌ Explainable AI
**SHAP Global Feature Importance**
This project implements two complementary explanation strategies:
- **SHAP (SHapley Additive exPlanations)** โ Provides global model interpretability by attributing each feature's contribution to predictions across the entire dataset. Clinicians can identify which biomarkers most consistently drive risk scores.
- **LIME (Local Interpretable Model-Agnostic Explanations)** โ Generates patient-level, instance-specific explanations by approximating the model locally. Each prediction comes with a ranked list of contributing factors for that individual.
Together, they provide 100% prediction coverage with no unexplained outputs.
---
## ๐ค AI & Model Transparency
This project uses machine learning components. In the interest of clinical trust and EU AI Act alignment:
- **Model Type**: Supervised classification (gradient-boosted ensemble โ inference from codebase)
- **Training Data**: Cleveland Heart Disease dataset (UCI ML Repository, publicly available)
- **External APIs**: None โ all inference runs locally
- **Determinism**: Predictions are deterministic given fixed model weights; SHAP/LIME outputs may vary slightly with sampling configurations
- **Known Limitations**: Trained primarily on the Cleveland dataset; performance may vary on populations significantly different from the training distribution. Not a substitute for clinical diagnosis.
- **Federated Learning**: Implemented for privacy-preserving multi-site training; FL accuracy is 85.9% vs. 94.1% centralized โ this trade-off is expected and disclosed.
- **User Data**: No patient data is stored, transmitted, or used for retraining in the deployed demo.
> **Disclosure**: Portions of this project's documentation were assisted by AI writing tools.
---
## ๐ ๏ธ Usage
### Running with Docker *(if applicable)*
```bash
docker build -t heart-disease-ai .
docker run -p 7860:7860 heart-disease-ai
```
### Environment Configuration
| Variable | Default | Description |
|:---------|:--------|:------------|
| `MODEL_PATH` | `healthcare_model/` | Path to trained model artifacts |
| `MLFLOW_TRACKING_URI` | `./mlruns` | MLflow experiment tracking directory |
| `LOG_LEVEL` | `INFO` | Logging verbosity (`DEBUG`, `INFO`, `WARNING`) |
| `PORT` | `7860` | Dashboard server port |
### MLflow Experiment Tracking
```bash
# View experiment results in MLflow UI
mlflow ui --port 5000
# Navigate to http://localhost:5000
```
---
## ๐ค Contributing
Contributions are welcome from clinicians, ML engineers, and healthcare AI researchers alike. Please read [CONTRIBUTING.md](CONTRIBUTING.md) before opening a PR.
### Development Setup
```bash
git clone https://github.com/Ariyan-Pro/ExplainableAI-HeartDisease.git
cd ExplainableAI-HeartDisease
pip install -r requirements.txt
```
### Submitting Changes
1. Fork the repo and create your branch: `git checkout -b feat/your-feature`
2. Make your changes and add tests where applicable
3. Verify nothing is broken: `python -m pytest`
4. Open a PR โ describe **what** you changed and **why**, not just what
### Areas Actively Welcoming Contributions
- Additional dataset support (MIMIC-III, Framingham)
- New explainability backends (Integrated Gradients, Captum)
- Multilingual dashboard support
- EHR integration connectors (FHIR, HL7)
---
## ๐ Security
Please do **not** open public GitHub issues for security vulnerabilities. Instead, report them privately by emailing the repository maintainer or opening a private security advisory via GitHub's security tab.
---
## ๐ License
[MIT](LICENSE) ยฉ 2024 [Ariyan Pro](https://github.com/Ariyan-Pro)
You are free to use, modify, and distribute this software for any purpose, including commercial clinical research, with attribution.
---
## ๐ Acknowledgments
- [UCI ML Repository](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) โ Cleveland Heart Disease Dataset
- [SHAP](https://github.com/slundberg/shap) โ Lundberg & Lee, NeurIPS 2017
- [LIME](https://github.com/marcotcr/lime) โ Ribeiro et al., KDD 2016
- [MLflow](https://mlflow.org/) โ Experiment tracking infrastructure
- [Gradio](https://gradio.app/) โ Interactive demo interface
- [FastAPI](https://fastapi.tiangolo.com/) โ Production API framework
---
## ๐ Resources
| Resource | Link |
|:---------|:-----|
| ๐ค Live Demo | [Hugging Face Spaces](https://huggingface.co/spaces/Ariyan-Pro/HeartDisease-Predictor) |
| ๐ Documentation | [`./docs`](./docs) |
| ๐ Issue Tracker | [GitHub Issues](https://github.com/Ariyan-Pro/ExplainableAI-HeartDisease/issues) |
| ๐ค Contributing Guide | [CONTRIBUTING.md](CONTRIBUTING.md) |
---
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