https://github.com/praveendecode/cardiosense_ai

Predicting heart disease risk using machine learning with clinical and demographic features and explore the factors influencing heart health and build predictive models.
https://github.com/praveendecode/cardiosense_ai

Last synced: about 1 month ago
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Predicting heart disease risk using machine learning with clinical and demographic features and explore the factors influencing heart health and build predictive models.

• Host: GitHub
• URL: https://github.com/praveendecode/cardiosense_ai
• Owner: praveendecode
• Created: 2023-09-13T17:34:41.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-10T17:53:26.000Z (5 months ago)
• Last Synced: 2025-02-09T13:35:05.251Z (3 months ago)
• Language: Python
• Size: 1.28 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Cardio Sense AI

    Predicting heart disease risk using machine learning with clinical and demographic features and explore the factors influencing heart health and build predictive models.

# Project Description:

    Cardio Sense AI is a machine learning project aimed at predicting the risk of heart disease based on a combination of clinical and demographic features. It also explores the factors that influence heart health, providing valuable insights for healthcare professionals and decision-makers. This project leverages data analytics and predictive modeling techniques to enhance patient outcomes and reduce healthcare costs.

# Goals of the Project:

  ## Predictive Modeling:

     Develop and train machine learning models to predict the risk of heart disease using clinical and demographic features.

  ## Data Exploration:

     Explore the dataset to identify patterns, correlations, and factors that contribute to heart health or disease.

  ## Animation :

     Create effective Animation using lottie to communicate insights and trends related to heart health.

  ## Decision Support:

     Assist hospital administrations in making informed decisions related to resource allocation and patient care strategies.

  ## Skills Covered:

    Data Preprocessing: Cleaning, transforming, and preparing data for analysis.

    Machine Learning: Building predictive models for healthcare risk assessment.

    Data Exploration: Identifying trends and patterns in healthcare data.

    Data Visualization: Creating clear and informative visuals for data presentation.

    Healthcare Analytics: Applying data analytics techniques to healthcare domain.

    Communication: Effectively conveying insights and recommendations to stakeholders.

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