diabTech: Predictive Type-2 Diabetes Modeling

Table of Contents

1. Overview
2. Key Technologies
3. Getting Started
   • Clone the Repository
   • Set Up Your Environment
4. Running the Application
   • Start the React Application
   • Start the Django Application

Overview

diabTech is a cutting-edge solution for predictive modeling of Type-2 Diabetes, integrating a sophisticated Random Forest Ensemble Model and a Multi Layer Perceptron Neural Network. These models are deployed in a robust Full Stack Web Application, harmonizing the capabilities of React, Django, and SQLite. Our goal is to provide a seamless, user-friendly interface for efficient diabetes risk prediction and management.

Key Technologies

SciKit Learn For advanced machine learning algorithms.	PyTorch For neural network optimization.	Redis For efficient caching and message brokering.
React For a dynamic and responsive frontend.	Django For a powerful and scalable backend.

Getting Started

Clone the Repository

To begin, clone the repository to your local machine:

git clone [URL of the repository]
cd [name of the repository]

Install Dependencies using Conda

1. Create the environment from the environment_final.yml file: conda env create -f environment_final.yml
2. Activate the new environment: conda activate diabetes_model_deployment_final
3. Verify that the new environment was installed correctly: conda env list

Run the React App

1. Navigate to the frontend directory: cd frontend/diabetesreactfrontend
2. Start the React App: npm start

Run the Django App

1. Navigate to the backend directory: cd backend/DjangoDiabetesBackend
2. Start the Django App: python manage.py runserver