Project HealthDoc

Bridging the gap between Patients and Doctors
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Table of Contents

• About the Project
  • Predictions
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Future Updates

About The Project

The aim of Project HealthDoc is to make the interaction of patients and doctors seamless, convenient and more organized. This platform is built keeping in mind the problems and hassles one has to face while using any medical facility via hospitals.

🤒 Here's what we provide for patients:

🧬 Disease prediction services

📄 Doctor appointment booking (Future Endeavour)

💊 Generic drugs to brand name converter & recommendation system (Future Endeavour)

Predictions

We used Random Forest Regression to train our models which predicts Heart and Liver Disease with accuracy of >90% for both.

Built With

Getting Started

Follow these simple steps to run the project locally:

Prerequisites

• Node.js
• Python 3.8+
• Yarn (we have used yarn for this project)

Installation

Django API

1. Clone the repo

   git clone https://github.com/roshan0708/HealthDoc
   

2. Install requirements

   cd DjangoAPI
   pip3 install -r requirements.txt
   

3. Start the server (by default it listens to http://127.0.0.1:8000/)

   python manage.py runserver
   

React + Node

1. Install React and Node modules

   cd ..   (only if not in root directory)
   yarn install
   cd client
   yarn install
   

2. Create a .env file in the root directory of the project. Add environment-specific variables on new lines in the form of NAME=VALUE by following same structure as provided in env.example file

    MONGO_URI=mongouri_here
    JWT_SECRET=jwtSecretkey
    SALT_ROUNDS=number_here
    EXPIRES_IN=jwtexpirytime
   
    # wrap SALT_ROUNDS inside Number() when using like this Number(SALT_ROUNDS)

3. Run the following command in the root directory to start the project

   yarn develop
   

🤩Viola! Project starts running at localhost:5000

Future Updates

• Appointment System
• Generic Medicine Name List
• More models for disease prediction
• Improve UI/UX overall
• Enable PWA

And More! There's always room for improvement!