https://github.com/sayande01/multiple_disease_prediction_system_machine_learning_webapp

The project aims to create a comprehensive web app using Streamlit, Anaconda, Jupyter Notebook, and Spyder, predicting Diabetes, Heart Disease, and Parkinson's. It uses ML models on diverse health data for personalized predictions, empowering users to manage health proactively.
https://github.com/sayande01/multiple_disease_prediction_system_machine_learning_webapp

spyder streamlit-webapp

Last synced: 6 months ago
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The project aims to create a comprehensive web app using Streamlit, Anaconda, Jupyter Notebook, and Spyder, predicting Diabetes, Heart Disease, and Parkinson's. It uses ML models on diverse health data for personalized predictions, empowering users to manage health proactively.

• Host: GitHub
• URL: https://github.com/sayande01/multiple_disease_prediction_system_machine_learning_webapp
• Owner: sayande01
• Created: 2024-04-28T12:17:30.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-28T12:20:10.000Z (over 1 year ago)
• Last Synced: 2025-02-13T02:39:40.696Z (8 months ago)
• Topics: spyder, streamlit-webapp
• Language: Jupyter Notebook
• Homepage:
• Size: 205 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
**Title:** 

"Streamlit Health Predictor: Comprehensive Disease Prediction Web Application"

**Description:**

Our project endeavors to develop an extensive web application using Streamlit, integrated with Anaconda, Jupyter Notebook, and Spyder, to predict the likelihood of multiple diseases, encompassing Diabetes, Heart Disease, and Parkinson's, based on diverse health parameters. Employing machine learning models trained on extensive medical datasets, the application offers tailored predictions, facilitating personalized healthcare decision-making. By providing an intuitive and accessible interface, our objective is to empower users to proactively manage their health, leveraging predictive insights to adopt preventive measures and enhance overall well-being.

**Objective:**

1. Integrate Streamlit with Anaconda, Jupyter Notebook, and Spyder for seamless development and deployment.

2. Develop machine learning models trained on comprehensive medical datasets for predicting Diabetes, Heart Disease, and Parkinson's.

3. Create an interactive web application using Streamlit to enable users to input health parameters and receive personalized disease predictions.

4. Enhance user engagement and accessibility through an intuitive and user-friendly interface.

5. Empower users to make informed healthcare decisions by providing personalized disease risk assessments.

6. Facilitate proactive health management by enabling users to adopt preventive measures based on predictive insights.

7. Foster a culture of preventive healthcare by promoting awareness and proactive health monitoring through the web application.