MediChateX

AI-Powered Personalized Medicine Recommendation System

The MediChateX is a full-stack application designed to manage medicine prescriptions, patient data, and ML-powered predictions. It consists of:

• A Flask backend for handling API requests, integrating machine learning models, and managing patient data.
• A React.js frontend for the web, providing a user-friendly interface to interact with the backend.
• A React Native mobile app that allows users to access the platform from their mobile devices.

Table of Contents

• Features
• Tech Stack
• Backend
• Frontend
• Mobile App
• Docker Setup
• How to Run
• License

Features

• Patient Management: Add, update, and view patient records.
• Medicine Prediction: Use machine learning to recommend medication based on the patient’s data.
• Multi-platform Support: Accessible via a web app (React.js) and mobile app (React Native).
• User Authentication: Login and Signup functionality for both web and mobile users.

Tech Stack

• Backend: Flask, Python, SQLite, Machine Learning (e.g., Scikit-Learn)
• Frontend: React.js, JavaScript, HTML, CSS
• Mobile App: React Native, JavaScript
• Database: SQLite
• Containerization: Docker, Docker Compose

Backend

The backend is built using Flask, a lightweight Python framework. It includes an API for managing patient data, user authentication, and medicine predictions, powered by a machine learning model.

Setup

1. Clone the repository:

   git clone <repo_url>
   cd medicine-app/backend
   

• app.py: This is the main Flask application file that handles API requests. It includes routes for user authentication, managing patient records, and recommending medicines using the trained ML model.

• train_model.py: This script trains the machine learning model using patient data and saves it as medicine_model.pkl. The model predicts recommended medicines based on user input.

• medicine_model.pkl: The trained machine learning model. It is used by the backend to make predictions on the user's data and recommend relevant medicines.

• label_encoder.pkl: The label encoder is used to convert categorical data (e.g., symptoms or conditions) into numerical data that the machine learning model can understand.

• patients.db: An SQLite database that stores patient records, including personal details and medical history. This data is used by the backend to make more personalized recommendations.

• requirements.txt: This file contains all the Python dependencies required for the backend, including Flask, Scikit-learn, and SQLite.

• README.md (Backend): The documentation for the backend, explaining how to set up, run, and interact with the backend.

Frontend

The frontend is a React.js web application that provides a user-friendly interface for interacting with the backend API. It is designed to manage patient records, perform user authentication, and display the recommended medicines.

Directory Structure

• public/: This folder contains static assets like index.html, logos, and other images required for the frontend.

• src/: This folder contains the source code for the React application.

  • components/: This subfolder contains reusable React components that make up different pages and sections of the application.

    • Login.js: The component responsible for the login page where users can authenticate.
    • Signup.js: The component responsible for the signup page where new users can register.
    • Navbar.js: The component that defines the navigation bar used across the app.
    • Home.js: The main page where users can input their symptoms to get medicine recommendations.
    • Results.js: This page displays the recommended medicines based on the user's input.

  • App.js: This is the root component that serves as the main container for the application.

  • App.css: The CSS file responsible for styling the React app.

  • index.js: The entry point of the React app, where it connects to the DOM and renders the app.

• package.json: This file lists the Node.js dependencies required for the frontend, such as React and React Router.

• README.md (Frontend): Documentation for setting up and running the React.js frontend.

Setup

1. Navigate to the frontend directory:

   cd frontend
   
   

This markdown structure includes all relevant information, proper code formatting, and clear steps for setting up the project. It will display well in the README.md file when rendered on platforms like GitHub.