TechieYan Technologies

Multiple Disease Prediction System Using Machine Learning


This project uses SVM which is a Machine learning algorithm to predict multiple diseases which have real time application, this project is basically divided into three steps i) Model trading ii) giving input to the model using camera iii) prediction. The main aim of the project is to predict the person is having disease based on input given to the model for doing the prediction streamlit framework is used.

Multiple Disease Prediction System

Code Description & Execution



Streamlit is a small and easy web framework which helps us to build beautiful websites. The main reason for using stream lit is that it offers very user-friendly experience and we don’t need to have a prior knowledge of HTML, CSS and JAVASCRIPT. Streamlit is mostly used for deploying machine learning models without using any external cloud integrations. Some of the applications of Streamlit are it helps to deploy Machine learning and deep learning models, it can also help us to build a front end for a normal code. The output can be viewed as local server in your web browser.

How to Execute?

So, before execution we have some pre-requisites that we need to download or install i.e., anaconda environment, python and a code editor. Anaconda: Anaconda is like a package of libraries and offers a great deal of information which allows a data engineer to create multiple environments and install required libraries easy and neat.

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Python: Python is a most popular interpreter programming language, which is used in almost every field. Its syntax is very similar to English language and even children and learning it nowadays, due to its readability and easy syntax and large community of users to help you whenever you face any issues.

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Code editor: Code editor is like a notepad for a programming language which allows user to write, run and execute program which we have written. Along with these some code editors also allows us to debug, which usually allows users to execute the code line by line and allows them to see where and how to solve the errors. But I personally feel visual code is very good to work with any programming language and makes a great deal of attachment with user.

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Steps to Execute

Note: Make sure you have added path while installing the software’s.

Install the prerequisites mentioned above.


Open anaconda prompt and create a new environment. To create an environment use the commands given below. Replace env_name by the name of environment you want to give.

  • conda create -n “env_name”
  • conda activate “env_name”


Set up jupyter notebook for your environment

  • conda install –c conda-forge jupyterlab
  • conda install –c anaconda ipython


Install necessary libraries from requirements.txt file provided.

Multiple Disease Prediction System file

Go to the directory where your requirement.txt file is present.

  • D:\Exicuted Projects\Multiple Disease Prediction System
Multiple Disease Prediction System command line
  • Run command  pip install -r requirements.txt or conda install requirements.txt

Requirements.txt is a text file consisting of all the necessary libraries required for executing this python file. If it gives any error while installing libraries, you might need to install them individually. All the required files will be downloaded after you run it. I got requirement already satisfied as I already have them installed.

Multiple Disease Prediction System code


To run the code, type “streamlit run “path/filename.extension” in our case streamlit run “D:\Exicuted Projects\Multiple Disease Prediction System\”

Multiple Disease Prediction System

Data Description

In this project we will be doing multiple disease prediction the diseases are Diabetes, Heart disease and Parkinsons al the data sets used are tabular data 

Diabetes dataset

Multiple Disease Prediction System diabetic

Heart dataset

Multiple Disease Prediction System

Parkinson dataset


Multiple Disease Prediction System result
Multiple Disease Prediction System result

Similarly other predictions can also be done

Issues Faced

  1. Save the model with proper name after training to avoid confusion.

Click Here To Download This Code And Associated File.


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