Healthcare Recommendation System

This project implements a comprehensive Healthcare Recommendation System that leverages machine learning and big data tools to provide symptom-based disease prediction and personalized health advice.

Table of Contents

• Overview
• Features
• Technologies Used
• Project Structure
• How to Run
• Results
• Contributors

Overview

The Healthcare Recommendation System is designed to assist users in identifying potential diseases based on symptoms and providing personalized recommendations for medications, diets, workouts, and precautions.

It integrates a Flask-based web application with a Spark-powered backend for real-time predictions and efficient data processing.

Features

• Symptom-based Disease Prediction: Predict diseases using a pre-trained Random Forest Classifier.
• Personalized Recommendations:
  • Descriptions of the predicted disease.
  • Suggested medications, diets, workouts, and precautions.
• Interactive Web Interface: User-friendly interface powered by Flask.
• Scalable Backend: Utilizes Apache Spark for processing large datasets efficiently.

Technologies Used

• Frontend:
  • HTML with Jinja2 templating (via Flask).
• Backend:
  • Flask for web services.
  • Apache Spark for machine learning and data processing.
  • Python libraries: NumPy, Pandas, Pickle.
• Machine Learning:
  • Random Forest Classifier for disease prediction.
  • PySpark's MLlib for building and evaluating the model.
• Datasets:
  • Symptom-disease relationships.
  • Disease-specific medications, diets, precautions, and workouts.

Project Structure

project-directory/
|-- datasets/
|   |-- symptoms_df.csv
|   |-- precautions_df.csv
|   |-- medications.csv
|   |-- diets.csv
|   |-- description.csv
|   `-- workout_df.csv
|-- models/
|   `-- svc.pkl
|-- app.py         # Flask application
|-- model.py       # Model training script
|-- templates/
|   |-- index.html
|   |-- about.html
|   |-- contact.html
|   `-- developer.html
|-- static/        # Static assets (CSS, JS, images)
`-- README.md

How to Run

Prerequisites

• Python 3.8+
• Apache Spark
• Flask
• Required Python libraries:

  pip install flask pyspark pandas numpy

Steps

1. Clone this repository:

   git clone https://github.com/anna123venkat/Healthcare-Recommendation-System.git
   cd Healthcare-Recommendation-System

2. Prepare the datasets:
   • Place the CSV files in the datasets/ directory.
3. Train the model (if needed):

   python model.py

4. Start the Flask server:

   python app.py

5. Open the application in your browser:

   http://localhost:5000
   

Results

Sample Output

• Home Page
• Symptom Input:
• Predicted Disease with Recommendations:

Contributors

• Abhishek S
• Manoj S
• Prasanna Venkatesh S