Cardio-Care

Cardio-Care is a comprehensive project aimed at detecting heart disease using machine learning algorithms. The project implements three different algorithms to predict the presence of heart disease: Logistic Regression, K-Nearest Neighbors (KNN), and Decision Tree. Each of these algorithms has been fine-tuned to provide the best possible accuracy:

Logistic Regression: Achieved an accuracy of 83.69%. This algorithm is suitable for binary classification tasks and is known for its efficiency and performance in distinguishing between two classes.
K-Nearest Neighbors (KNN): Achieved an accuracy of 67.75%. KNN is a simple, non-parametric, and lazy learning algorithm that classifies data points based on their proximity to other data points.
Decision Tree: Achieved an accuracy of 78.98%. This algorithm uses a tree-like model of decisions and their possible consequences, making it useful for both classification and regression tasks.

Features

Heart Disease Detection: Uses machine learning algorithms to predict the likelihood of heart disease.
User Input Form: Collects necessary patient information for the prediction.
Algorithm Comparison: Displays the performance of different algorithms used.
Responsive Design: Optimized for various devices, ensuring a seamless experience on mobile phones, tablets, and desktops.

Technologies Used

Python: Core programming language for implementing machine learning algorithms.
Jupyter Notebook: For interactive development and testing of machine learning models.
HTML, CSS, JavaScript: For creating the front-end of the web application.
Streamlit: For building and deploying the web application.
Scikit-learn: For implementing machine learning algorithms.

Installation

To get a local copy up and running, follow these steps:

Clone the repository:

git clone https://github.com/your-username/cardio-care.git

Navigate to the project directory:
```
cd cardio-care
```

Running the Jupyter Notebook

Install Python dependencies:
```
pip install -r requirements.txt
```
Run the Jupyter Notebook:
```
jupyter notebook Cardio_Care.ipynb
```

Running the Detection Script

Install Python dependencies:
```
pip install -r requirements.txt
```
Run the detection script:
```
python detection.py
```

Usage

Input Patient Data: Use the provided form to input patient details such as age, gender, chest pain type, resting BP, cholesterol, fasting BP, resting ECG, max heart rate, exercise angina, old peak, and ST slope.
Run Prediction: Submit the form to run the prediction model and get results based on the input data.
View Results: The application will display the predicted outcome along with the probabilities from different algorithms.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

Fork the Project.
Create your Feature Branch (git checkout -b feature/AmazingFeature).
Commit your Changes (git commit -m 'Add some AmazingFeature').
Push to the Branch (git push origin feature/AmazingFeature).
Open a Pull Request.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Your Name - @your-twitter-handle - your-email@example.com

Project Link: https://github.com/your-username/cardio-care

anurag050301 / Cardio-Care