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Disease Prediction using Machine Learning

Introduction

  • This project is based on the idea of predicting the disease of a patient based on the symptoms he/she is facing.
  • The dataset used for this project is taken from Kaggle.
  • The dataset contains 4920 rows and 132 columns.
  • The dataset contains the name of the disease, the symptoms of the disease and the description of the disease.
  • The dataset is in the form of a csv file.

Procedure

  • The dataset is cleaned and preprocessed.
  • The dataset is then split into training and testing data.
  • The training data is then used to train the model.
  • The model is then tested on the testing data.
  • The model is then used to predict the disease of a patient based on the symptoms he/she is facing.

Technologies Used

ML

  • Python
  • Scikit-learn
  • Pandas
  • Numpy

Web

  • React
  • Node.js
  • Express.js

How to run the project

  • Clone the repository.
  • Open the terminal in the project directory.
  • Run the following commands:
cd server
npm install
npm start
  • The server will be running on localhost:5000.
  • Open another terminal in the project directory.
  • Run the following commands:
cd frontend
npm install
npm start
  • The project will be running on localhost:3000.

NOTE : For linux users, in server/server.js file , in line 14 change the command spawned to python3 or python instead of py.

Project Architecture

Frontend

  • The frontend is built using React.
  • The frontend is divided into 2 components:
    • Home
    • Predict
  • The Home component is the landing page of the website.
  • The Predict component is used to predict the disease of a patient based on the symptoms he/she is facing.
  • Symptoms are selected from the list of symptoms.
  • The selected symptoms are then sent to the backend.
  • The predicted disease is then displayed on the screen.

Backend

  • The backend is built using Node.js and Express.js.
  • The backend contains server.js file which is the entry point of the server.
  • It runs the python script and returns the predicted disease to the frontend.
  • The python script is run using the child_process module of Node.js.

Contributors