Heart Failure Risk Prediction Web Application

This project is a web application developed using Flask, a Python web framework, for predicting the risk of heart disease and providing personalized lifestyle recommendations based on user input.

Features:

• Heart Disease Prediction: Utilizes a Random Forest Classifier trained on a dataset containing various heart health parameters to predict the likelihood of heart disease based on user input such as age, sex, cholesterol levels, blood pressure, etc.
• Personalized Lifestyle Recommendations: Offers tailored lifestyle recommendations to mitigate heart disease risk factors, considering factors like physical activity, diet, smoking habits, medical history, and more.
• Interactive Web Interface: Provides an intuitive web interface for users to input their data and receive predictions and recommendations instantly.
• Email Report Generation: Allows users to generate and send a detailed medical report to their healthcare provider for further evaluation.
• AI chatbot intgration: Talk to your personal healthcare assistant hridaya for any queries.

Technologies Used:

• Python: Utilized for backend development, including data processing, model training, and web server implementation.
• Flask: Chosen as the web framework for its simplicity, flexibility, and scalability in building web applications.
• Pandas and NumPy: Employed for data manipulation and preprocessing tasks.
• Scikit-learn: Used for building and training the Random Forest Classifier model for heart disease prediction.
• HTML and CSS: Implemented the frontend interface for user interaction and styling.
• JavaScript: Included for client-side scripting and interaction with the Botpress chatbot integration.

Project Snippets

Usage:

1. Clone the Repository: Clone this repository to your local machine using git clone https://github.com/abhii04/hridaycare.git
2. Run the Application: Start the Flask web server by running python test.py in the project directory.
3. Access the Application: Open your web browser and navigate to http://localhost:5000 to access the web application.

Contributing:

Contributions are welcome! Feel free to open issues for bug fixes, feature requests, or any other improvements you'd like to see. Pull requests are also appreciated.