Outlier Detection in Healthcare Data

This project focuses on detecting outliers in healthcare data using machine learning techniques. Identifying outliers in clinical data can help in early diagnosis of errors, fraud, and abnormal health conditions.

Features

• Identifies anomalies in patient records, lab results, and other healthcare data.
• Utilizes advanced techniques like Isolation Forest, DBSCAN, and Autoencoders for outlier detection.
• Helps reduce medical errors and identify fraudulent activities in healthcare systems.
• Scalable solution that handles large datasets with various data types.

Installation

To set up the Outlier Detection in Healthcare Data system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/outlier-detection-healthcare.git
   cd outlier-detection-healthcare

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start detecting outliers in healthcare data, run the main script:

python outlier_detection.py

The script will analyze healthcare data, detect outliers, and provide a detailed report on anomalies.

Results

The system successfully detects anomalies in healthcare data, helping healthcare providers identify potential errors, fraudulent entries, and abnormal health conditions.