https://github.com/rayyan9477/claim-guard-healthcare-claim-integrity-and-fraud-detection

ClaimGuard is a smart system designed to help medical billing companies and hospitals ensure the integrity of their healthcare claims and prevent denials. Think of it as a sophisticated tool that analyzes claim data to predict whether an insurance company is likely to reject a claim before it's even submitted.
https://github.com/rayyan9477/claim-guard-healthcare-claim-integrity-and-fraud-detection

claims exploratory-data-analysis fraud-detection machine-learning medical-billing python random-forest

Last synced: 4 months ago
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ClaimGuard is a smart system designed to help medical billing companies and hospitals ensure the integrity of their healthcare claims and prevent denials. Think of it as a sophisticated tool that analyzes claim data to predict whether an insurance company is likely to reject a claim before it's even submitted.

• Host: GitHub
• URL: https://github.com/rayyan9477/claim-guard-healthcare-claim-integrity-and-fraud-detection
• Owner: Rayyan9477
• Created: 2025-01-31T15:16:04.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2025-02-13T20:30:31.000Z (4 months ago)
• Last Synced: 2025-02-13T21:29:51.199Z (4 months ago)
• Topics: claims, exploratory-data-analysis, fraud-detection, machine-learning, medical-billing, python, random-forest
• Language: Python
• Homepage: https://claims-optimization.streamlit.app/
• Size: 298 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ClaimGuard: Healthcare Claim Integrity and Fraud Detection

## Description:

ClaimGuard is a smart system designed to help medical billing companies and hospitals ensure the integrity of their healthcare claims and prevent denials. Think of it as a sophisticated tool that analyzes claim data to predict whether an insurance company is likely to reject a claim *before* it's even submitted.

**What does ClaimGuard do?**

*   **Predicts Claim Denials:** Uses predictive analytics to identify potential issues that could lead to claim denials.

*   **Highlights Errors:** Points out specific areas of concern, such as incorrect coding, missing information, or the need for prior authorization.

*   **Provides Recommendations:** Suggests actions to take to fix the errors and increase the chances of approval.

**How does ClaimGuard benefit medical billing companies and hospitals?**

*   **Reduces Claim Denials:** By catching errors early, ClaimGuard helps prevent costly claim denials and rework.

*   **Accelerates Payments:** Faster claim approvals mean quicker payments and improved cash flow.

*   **Increases Revenue:** By minimizing denials and maximizing approvals, ClaimGuard helps boost revenue for healthcare providers.

*   **Improves Efficiency:** Automates the error detection process, freeing up staff to focus on other important tasks.

## Visuals

![Claim Amount Distribution ](https://github.com/Rayyan9477/Claim-Guard-Healthcare-Claim-Integrity-and-Fraud-Detection/blob/main/Claim_amount%20distribution.png)

![Denial Distribution](https://github.com/Rayyan9477/Claim-Guard-Healthcare-Claim-Integrity-and-Fraud-Detection/blob/main/Denial%20distribution.png)

![Insurance Provider's ](https://github.com/Rayyan9477/Claim-Guard-Healthcare-Claim-Integrity-and-Fraud-Detection/blob/main/Insurance_providers.png)

## Project Structure

*   **data/**: Contains the dataset used for analysis and model training.

    *   `dataset.csv`: Historical claims data including features such as patient age, gender, insurance provider, service codes, diagnosis codes, claim amounts, and claim statuses.

*   **models/**: Stores the trained predictive model.

    *   `model.pkl`: The trained machine learning model (Random Forest Classifier) that predicts claim denial risk.

*   **notebooks/**: Contains Jupyter notebooks for data analysis.

    *   `data_analysis.ipynb`: Exploratory data analysis (EDA) and visualizations to identify patterns in claim denials and errors.

*   **src/**: Source code for the application.

    *   `app.py`: Main application script, initializes the Streamlit user interface.

    *   `model_training.py`: Functions for training the predictive model and saving it.

*   **requirements.txt**: Lists the Python libraries required to run the project.

## Tech Stack

*   **Streamlit:** For creating the user-friendly web interface.

*   **pandas:** For data manipulation and analysis.

*   **scikit-learn:** For machine learning model training and prediction.

*   **matplotlib and seaborn:** For data visualization.

*   **joblib:** For saving and loading the trained model.

## Setup Instructions

1.  Clone the repository:

    ```bash

    git clone 

    cd claims-optimization

    ```

2.  Install the required packages:

    ```bash

    pip install -r requirements.txt

    ```

3.  Run the application:

    ```bash

    streamlit run src/app.py

    ```

## Usage Guidelines

1.  **Access the ClaimGuard App:** Once the app is running, it will open in your web browser.

2.  **Input Claim Details:** Enter the claim details in the input fields provided.

3.  **Predict Claim Status:** Click the "Predict Claim Status" button to get a prediction.

4.  **Review the Prediction:** The app will display whether the claim is likely to be "Approved" or "Denied".

5.  **Explore Data Analysis:** Use the sidebar to toggle the Exploratory Data Analysis (EDA) section. This section provides visualizations to help you understand the data.

## Contributing

We welcome contributions to make ClaimGuard even better! If you have ideas for new features, improvements, or bug fixes, please fork the repository and submit a pull request.

## Contact

**Rayyan Ahmed**

*   GitHub: [https://github.com/Rayyan9477](https://github.com/Rayyan9477)

*   LinkedIn: [https://www.linkedin.com/in/rayyan-ahmed9477/](https://www.linkedin.com/in/rayyan-ahmed9477/)

*   Email: [email protected]