HeartWise : Heart Attack Risk Detection

Demo

video5812017601135514493_yfjvpfXg.mp4

👥 Our Team

• Ahmed Sakkijha
• Nahla bader
• Lara Abed
• Abdallah Elahakawti
• Malik Alkhalil

🎯 Why This Project?

Heart Attacks's affects millions worldwide, often going undiagnosed until it has significantly progressed. Early diagnosis is key to improving the quality of life and enabling better treatment options. This project focuses on bridging that gap by offering a reliable detection tool that can assist healthcare professionals in identifying cases in the early stages of advanced Alzheimer's.

⚙️ How It Works

Data Collection

We used a public dataset found on kaggle.com, of course this data was reviewed by doctors who agreed on its quality.

Model Training

Using machine learning algorithms, such as SVM (SVC) and RandomForestClassifier , the model is trained to detect Heart Attacks's disease patterns.

Evaluation

The model's performance is evaluated using metrics like accuracy, cross_val_score, ensuring a balance between correctly detecting the disease and minimizing false positives.

🧬Features

• High Accuracy: Our model achieves 87% accuracy in detecting Heart Attacks's at critical stages.
• Easy Integration: Plug this tool into any healthcare system for smooth integration with existing patient records.
• User-Friendly Interface: Designed for healthcare professionals with a clear, intuitive interface.

🛠️ Tech Stack

• Programming Language: Python

• Libraries: SVC , pickle , joblib , Pandas, NumPy , Matplotlib, sklearn ,RandomForestClassifier , Streamlit

• Tools: Jupyter Notebook, Git , Colab

To use the tool you need to install some dependencies.

pip install -r requirements.txt