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---
title: "AIO2025M03 Heart Disease Diagnosis"
emoji: "🫀"
colorFrom: "blue"
colorTo: "red"
sdk: "gradio"
sdk_version: "5.38.2"
app_file: "app.py"
pinned: false
---
# 🫀 Cleveland Heart Disease Diagnosis (Ensemble Demo)
An interactive demo that predicts heart disease risk from patient features using an ensemble of ML models trained on the Cleveland dataset.
## ✨ Features
- **🧩 Ensemble models**: Decision Tree, k-NN, and Naive Bayes with soft voting
- **📋 Validation metrics**: ROC-AUC, Accuracy, Precision, Recall, F1 (80/20 stratified split)
- **🧪 Patient inputs**: 13 clinical features with sensible defaults and example selector
- **📈 Results visualization**: Color/emoji-coded prediction confidence bar chart per model
- **ℹ️ Clear context**: Styled About card and fixed disclaimer (educational use only)
- **🎨 Theming**: VLAI template with configurable colors, fonts (Inter), header/footer
## 🚀 Quick Start
### Local Installation
```bash
git clone <repository-url>
cd AIO2025M03_HEART_DISEASE_PREDICTION
pip install -r requirements.txt
python app.py
```
### Usage
1. Ensure `data/cleveland.csv` exists (included in this repo)
2. Launch the app: `python app.py`
3. Review the About card and disclaimer at the top
4. Inspect data preview and validation metrics (trained at startup, 80/20)
5. Enter patient features or pick an example, then click Predict
6. Read individual model results and the predictions overview chart
## 🧠 Technical Highlights
- **Preprocessing**: Numeric median imputation; categorical most-frequent + one-hot
- **Pipelines**: Shared preprocessor wrapped with each estimator; ensemble via `VotingClassifier`
- **Evaluation**: Stratified train/validation split, with ROC-AUC and common classification metrics
- **Visualization**: Plotly bar chart; Gradio Blocks UI using VLAI template
## 📋 Requirements
- Python 3.8+
- Gradio 5.38+
- Scikit-learn
- Pandas
- NumPy
- Plotly
## 🎨 Theming & Branding
The UI is powered by `vlai_template.py` and can be customized programmatically:
```python
import vlai_template as vt
vt.configure(
project_name="Heart Disease Diagnosis Project",
year="2025",
module="03",
description=(
"Predict heart disease risk from patient data with ML models "
"trained on the Cleveland dataset."
),
colors={
"primary": "#0F6CBD",
"accent": "#C4314B",
"bg1": "#F0F7FF",
"bg2": "#E8F0FA",
"bg3": "#DDE7F8",
},
font_family=(
"'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, "
"'Helvetica Neue', Arial, 'Noto Sans', 'Liberation Sans', sans-serif"
),
meta_items=[
("Dataset", "Cleveland Heart Disease"),
("Models", "Decision Tree, k-NN, Naive Bayes"),
("Ensemble", "Soft Voting"),
],
)
```
You can also inject the info and disclaimer blocks:
```python
gr.HTML(vt.render_info_card(icon="🫀", title="About this demo"))
gr.HTML(vt.render_disclaimer(
text=(
"This interactive heart disease prediction demo is provided strictly "
"for educational purposes and is not intended for clinical use."
)
))
```
## 📄 License
Educational use for AIO2025 course materials. This demo is not medical advice.
---
**Live Demo**: coming soon
|