https://github.com/mainakverse/cardiac-arrest-risk-detection

Cardiac Arrest Risk Detection with binary data types
https://github.com/mainakverse/cardiac-arrest-risk-detection

generative-ai machine-learning python3 streamlit-webapp

Last synced: about 1 year ago
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Cardiac Arrest Risk Detection with binary data types

• Host: GitHub
• URL: https://github.com/mainakverse/cardiac-arrest-risk-detection
• Owner: MainakVerse
• Created: 2025-02-09T13:51:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-27T02:16:02.000Z (over 1 year ago)
• Last Synced: 2025-02-27T03:23:30.457Z (over 1 year ago)
• Topics: generative-ai, machine-learning, python3, streamlit-webapp
• Language: Python
• Homepage: https://cardiac-arrest-risk-detection.streamlit.app/
• Size: 709 KB
• Stars: 9
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
# Cardiac Arrest Risk Detection Programme

![cardiac](https://github.com/user-attachments/assets/5324d986-0da6-4720-8bd9-3f9515292728)

Cardiac arrest, an abrupt cessation of cardiac activity, can evoke apprehension. Understanding its pathophysiology and imperative interventions for patients in distress is paramount. Upon the onset of cardiac arrest, blood flow to the body and brain diminishes rapidly. Early recognition and management improve survival chances. Automated External Defibrillators (AEDs) administer life-saving shocks, returning erratic heartbeats to a normal rhythm. 

Driven by cutting-edge technology, artificial intelligence offers immense potential in safeguarding cardiac health. With groundbreaking algorithms, AI can analyze data swiftly to determine subtleties often difficult for doctors, recognizing unusual rhythms in electrocardiograms before events escalate into with cardiac arrest. Utilizing complex modeling, AI foreputs both refined prognostic assessments and efficient incident response capabilities enabling the provision of primoires ensuring optimal patient well-being among those at peril through mitigatory preventative checkups unaware of concealed underlying contexts accentuated these enhancements

## Tech Used:

- Python (3.12 or higher)

- Streamlit

- React JS

- Cron JOB

- Gemini AI API

## Modules:

- Home Page

- Ask Query

- Diagnosis

- Result Visualization

- Knowledge Center (Upcoming)

- Suggestion Box (Upcoming)