https://github.com/ascender1729/healthfraudmlchain
This project focuses on addressing healthcare insurance fraud using advanced technologies such as machine learning and blockchain. It explores the use of various machine learning algorithms for detecting fraudulent activities in healthcare insurance claims and employs blockchain technology to ensure data integrity.
https://github.com/ascender1729/healthfraudmlchain
analytics blockchain claim-processing fraud-prevention healthcare-fraud-detection healthcare-technology machine-learning security
Last synced: over 1 year ago
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This project focuses on addressing healthcare insurance fraud using advanced technologies such as machine learning and blockchain. It explores the use of various machine learning algorithms for detecting fraudulent activities in healthcare insurance claims and employs blockchain technology to ensure data integrity.
- Host: GitHub
- URL: https://github.com/ascender1729/healthfraudmlchain
- Owner: ascender1729
- License: other
- Created: 2023-12-10T10:25:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-01T10:05:35.000Z (over 2 years ago)
- Last Synced: 2025-02-06T13:39:30.959Z (over 1 year ago)
- Topics: analytics, blockchain, claim-processing, fraud-prevention, healthcare-fraud-detection, healthcare-technology, machine-learning, security
- Language: Jupyter Notebook
- Homepage: https://github.com/ascender1729/HealthFraudMLChain
- Size: 49.2 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# HealthFraudMLChain
## Overview
HealthFraudMLChain is a pioneering web application designed to combat healthcare insurance fraud through the integration of machine learning and blockchain technologies. It aims to enhance the detection and prevention of fraudulent activities, ensuring the security and integrity of healthcare insurance claims.
## Features
- **User-Friendly Web Interface**: Built with Flask, the application provides an easy-to-navigate interface for user interactions, including account management and policy oversight.
- **Advanced Fraud Detection**: Utilizes machine learning models to accurately identify and predict fraudulent activities in healthcare claims, significantly improving fraud prevention efforts.
- **Blockchain Integration**: Employs blockchain technology for creating an immutable ledger, ensuring the transparency and verifiability of transactions and enhancing data security.
- **Data Encryption**: Implements ECIES for robust encryption, protecting sensitive information against unauthorized access.
These streamlined features underscore HealthFraudMLChain's commitment to leveraging advanced technologies for safeguarding healthcare insurance processes against fraud.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Table of Contents](#table-of-contents)
- [HealthFraudMLChain Setup Guide](#healthfraudmlchain-setup-guide)
- [Installation & Setup](#installation--setup)
- [Running the Application](#running-the-application)
- [Shutting Down](#shutting-down)
- [Usage](#usage)
- [Tools and Technologies](#tools-and-technologies)
- [Development Tools and Technologies Table](#development-tools-and-technologies-table)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## HealthFraudMLChain Setup Guide
### Installation & Setup
1. **Clone the Repository**:
Open PowerShell and navigate to the directory where you want to clone the repository.
```powershell
git clone https://github.com/ascender1729/HealthFraudMLChain.git
```
2. **Navigate to the Project Directory**:
```powershell
cd .\HealthFraudMLChain\code\
```
3. **Create and Activate the Virtual Environment**:
This step is important to ensure that the Python packages installed do not interfere with the packages of other Python projects.
```powershell
python -m venv myenv
.\myenv\Scripts\Activate.ps1
```
4. **Install Dependencies**:
Once the virtual environment is activated, you'll see `(myenv)` before your directory path in the terminal. Now, install the project dependencies.
```powershell
pip install -r requirements.txt
```
### Running the Application
1. **Set Flask Environment Variables**:
Before running the Flask application, you need to set two environment variables. The `FLASK_APP` variable points to your main application file, and the `FLASK_ENV` sets the environment (development/production).
```powershell
$env:FLASK_APP = "main.py"
$env:FLASK_ENV = "development"
```
2. **Start the Flask Application**:
To run the Flask application, use the `flask run` command. This will start a local server.
```powershell
flask run
```
You should see output indicating the server has started, similar to this:
```
* Serving Flask app 'main.py'
* Debug mode: off
* Running on http://127.0.0.1:5000
```
3. **Accessing the Application**:
Open your web browser and go to `http://127.0.0.1:5000` to view and interact with the Flask application.
### Shutting Down
1. **Deactivate the Virtual Environment**:
When you are finished working with your Flask application, you can deactivate the virtual environment to return to your global Python environment.
```powershell
deactivate
```
Remember to deactivate your virtual environment (`deactivate`) before closing PowerShell or navigating away from the project directory.
## Usage
The application provides several endpoints for interaction:
- `/login`: User login page.
- `/signup`: User signup page.
- `/index`: Main interface for entering and managing policy information.
- Additional endpoints for blockchain integrity checks and other operations.
## Tools and Technologies
### Development Tools and Technologies Table
Development Area
Tools/Technologies
Description
For structuring and styling web pages, ensuring an intuitive and responsive user interface.
A templating engine for Python, used for generating HTML pages with dynamic content.
A lightweight WSGI web framework for serving the web application.
The core programming language, used across backend development and data processing tasks.
Essential for data manipulation and analysis, enabling efficient handling of datasets.
Used for developing predictive models to identify fraudulent activities.
Supports high-level mathematical functions and multi-dimensional arrays.
Utilized for creating immutable data records, enhancing data security and integrity.
For secure data encryption and decryption, and generating Ethereum-compatible keys.
Implements secure hash and message digest algorithms, vital for data integrity checks.
Enhances data security through Elliptic Curve Cryptography.
For handling data in CSV and JSON formats.
For performing operating system level operations and managing warnings respectively.
Empowers source code management and collaborative development.
Hosts the project repository, providing a platform for version control and collaboration.
## License
This work is licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](LICENSE.md).
## Acknowledgments
- Dr. Rajesh Kumar Sinha for guidance and support.
- National Institute of Technology Patna for providing the platform for research.