E-Health-Care

The web app is designed to reduce dependence on doctors, assist underserved individuals with basic medical checkups, and help people avoid unnecessary medical expenses. This project is built with Django, machine learning algorithms, and deep learning (ANN and CNN).

Overview

In many regions, particularly in Nepal, there is a significant shortage of doctors. Many people lack access to proper medical checkups, and numerous cases lead to severe outcomes due to a lack of timely medical intervention. This app addresses these issues by providing an accessible solution for basic medical checkups.

Demo

Application

• Reduce dependence on doctors.
• Assist underserved individuals with essential medical checkups.
• Minimize unnecessary medical expenses.
• Promote technology’s role in healthcare.

Models

The project includes several models for disease prediction:

• Pneumonia model: pickle_model_pneumonia.pkl
• Diabetes model: pickle_model_diabetes.pkl
• Heart model: pickle_model_heart.pkl
• Disease model: pickle_model_disease.pkl

Kernels Used for Training Deep Learning Models

• Kaggle Kernel for Malaria model: Link
• Kaggle Kernel for Pneumonia model: Link

Datasets Used for Model Development

• Heart: heart.csv [In the repository]
• Diabetes: diabetes.csv [In the repository]
• Disease: disease.csv [In the repository]
• Pneumonia: Link

Tools Used for Project Development

• Python (3.7 or higher)
• Django
• JavaScript
• Pandas
• NumPy
• HTML
• CSS

Installation

This project requires Python 3.7 or higher. If Python is not installed, download it here.

Step 1: Install Dependencies with UV

The project uses UV for Python dependency management, which allows for quick and efficient package resolution. Install UV by following instructions on their official site.

1. Install dependencies using UV: Linux:-

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

   Windows:-

   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
   

2. Run initial database migrations:

   uv run python manage.py migrate
   

3. Create a superuser for accessing the admin interface:

   uv run python manage.py createsuperuser
   

4. Start the development server:

   uv run python manage.py runserver
   

User Setup

1. After creating the superuser, log in to the admin panel at http://127.0.0.1:8000/admin.
2. Create a user group named ADMIN.
3. Add new users, setting their is_active and is_staff to True, and assign them to the ADMIN group for admin privileges.
4. These users can now log in from the main dashboard with admin access.

Features

Patient

• Register and sign in
• Update and edit profile
• View and search specialist doctors
• Predict diseases based on symptoms or x-ray images
• Book, cancel, view, and download appointments
• Provide feedback to the system

Admin

• View and manage total patients, predictions, doctors, and feedback
• Add, edit, delete, and assign doctors to respective diseases

Doctor

• Register via provided username and password from the admin
• Manage appointments and view patient records
• Send disease precautions to patients via email

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss proposed changes.

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