🧠 Heart Disease Prediction using Artificial Neural Network (ANN)

This project uses an Artificial Neural Network (ANN) to predict whether a patient is likely to have heart disease based on various medical attributes. It includes both a Jupyter Notebook for training and a Streamlit Web App for real-time prediction.

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🌐 Live App

👉 Click here to try the app

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📊 Dataset

The dataset includes clinical features such as:

• Age
• Gender
• Chest pain type
• Resting blood pressure
• Cholesterol
• Maximum heart rate
• Exercise-induced angina
• ST depression
• And more

Target:

• 0 → No heart disease
• 1 → Heart disease present

📁 Dataset Source:
Kaggle - Heart Disease Dataset

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🧠 Model Architecture

Layer Type	Details
Input Layer	13 input features
Hidden Layers	Dense layers with ReLU activations
Output Layer	1 neuron with Sigmoid activation

• Loss Function: Binary Crossentropy
• Optimizer: Adam
• Metrics: Accuracy

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📈 Model Performance

• ✅ Training Accuracy: ~98.26%
• ✅ Testing Accuracy: ~84.61%
• 🧪 Slight overfitting observed

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🛠️ Technologies Used

• Python
• TensorFlow / Keras
• Streamlit
• Pandas, NumPy
• Matplotlib, Seaborn
• Jupyter Notebook

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📁 Files Included

File	Description
Heart_ANN.ipynb	Notebook: training, evaluation, visualization
heart_disease_app_py.py	Streamlit web interface
heart_ann_model.h5	Trained ANN model (Keras H5 format)
requirements.txt	Project dependencies

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🚀 How to Run the App Locally

# Clone the repository
git clone https://github.com/codesbysuraj/heart-disease-detect-ann.git
cd heart-disease-detect-ann

# Install dependencies
pip install -r requirements.txt

# Launch the Streamlit app
streamlit run heart_disease_app_py.py

👨‍💻 Author

Suraj Yadav

• GitHub: @codesbysuraj
• Email: surajy.tech@gmail.com