Symptom-to-Disorder Matching Application

This project is a symptom-to-disorder matching application using Hadoop/MapReduce and Streamlit. The application allows users to input symptoms and receive predictions for potential disorders based on a preprocessed healthcare dataset. dsp is the folder that contains all of the files needed to run the application. dsp stands for Disease - Symptom Prediction.

Project Overview

The application processes a dataset of disorders and associated symptoms to create a mapping between symptoms and likely disorders. This mapping is then used by a Streamlit dashboard to provide symptom-based predictions.

Key Components

• Hadoop MapReduce: Processes the dataset to generate a mapping between symptoms and disorders.
• Docker: Containerizes the application to enable easy setup and consistent deployment.
• Streamlit: Hosts the user interface where users can enter symptoms and view potential disorders.

Prerequisites

• Docker: Ensure Docker is installed and running. Download Docker.

Getting Started

Follow these steps to set up and run the application:

1. Extract the Project Files

   • Unzip the project folder to your local machine.
   • Open a terminal and navigate to the extracted project folder (the root folder should contain the docker-compose.yml file).

   cd path/to/dsp

2. Run the Application

   • Start the application by running the following command in the project root directory:

     docker compose up

   This command will initialize three containers:

   • Namenode: Executes the MapReduce job to process the dataset.
   • Datanode: Supports data storage within the Hadoop Distributed File System (HDFS).
   • Streamlit: Hosts the user-facing dashboard.

3. Access the Application

   • Once the containers are running and this line shows up in the terminal: namenode INFO streaming.StreamJob: Output directory:/user/root/output/symptom_disease_mapping_run2 open your web browser and navigate to:

     http://localhost:8501
     

   • The Streamlit dashboard should now be accessible. You can enter symptoms into the input field, and the app will display a list of potential disorders associated with those symptoms.

Stopping the Application

To stop the application, return to the terminal where docker compose up is running and press Ctrl + C. To fully clean up containers, networks, and volumes, run:

docker compose down