Lang Patient Database (lang-patient-db)

This project sets up a MongoDB database containing patient datasets to analyze comorbidities between hypertension and mental health conditions, focusing on depression and anxiety. The database is designed for healthcare analytics and research.

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Features

• Preprocessed datasets for hypertension and mental health conditions.
• Python script to automatically load datasets into MongoDB collections.
• Instructions to verify data and use it for analysis.

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Prerequisites

Before starting, ensure you have the following installed:

• Python (version 3.8 or higher)
• MongoDB (installed and running locally or accessible remotely)
• Required Python libraries (see requirements.txt)

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Installation and Setup

1. Clone the Repository

git clone https://github.com/your-username/lang-patient-db.git
cd lang-patient-db

2. Install Dependencies

Install the required Python libraries:

pip install -r requirements.txt

3. Load Data into MongoDB

Run the Python script to load the datasets into your MongoDB instance:

python setup_mongodb.py

4. Start MongoDB Shell

To confirm the data is loaded successfully, start the MongoDB shell and verify:

mongosh

Then run the following commands:

use health_data
show collections

You should see:

• hypertension
• depression_anxiety

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Project Structure

lang-patient-db/
├── setup_mongodb.py         # Script to load data into MongoDB
├── requirements.txt         # List of required Python libraries
├── README.md                # Project documentation
├── .gitignore               # Git ignore file
├── data/                    # Contains the datasets
│   ├── processed_depression_anxiety.csv
│   ├── final_hypertension_dataset.csv

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Datasets

1. processed_depression_anxiety.csv

• Contains data on depression and anxiety patients.
• Attributes: patient_id, age, gender, anxiety_level, depression_level, etc.

2. final_hypertension_dataset.csv

• Contains data on hypertension patients.
• Attributes: patient_id, age, gender, blood_pressure, medication_status, etc.

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Usage

1. Once the database is set up, you can access the data programmatically using MongoDB drivers for Python, such as pymongo.

2. Example query to fetch hypertension data:

from pymongo import MongoClient

# Connect to MongoDB
client = MongoClient("mongodb://localhost:27017/")
db = client["health_data"]

# Fetch hypertension collection
hypertension_data = db["hypertension"].find()

for record in hypertension_data:
    print(record)

3. Replace the query parameters to fit your analysis needs.

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Authors

• Aysha
• Shruti

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Contributing

Contributions, issues, and feature requests are welcome! Feel free to fork this repository and submit a pull request.

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License

This project is open source and available under the MIT License. See the LICENSE file for details.

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