DecAI Prognosis is a machine learning-powered application designed to predict the 10-year risk of mortality based on user-provided health parameters. This tool provides an intuitive interface for healthcare professionals and researchers to make data-driven predictions using pre-trained models.
- Interactive Interface: User-friendly interface created with Streamlit.
- Customizable Inputs: Accepts a variety of health-related inputs such as age, blood pressure, cholesterol levels, and more.
- Prediction: Provides a binary output indicating whether an individual is at risk or not at risk of mortality in the next 10 years.
- Data Logging: Saves user inputs and predictions to a CSV file for future analysis.
- Dark Theme: Modern, dark-themed design for better user experience.
- Python 3.8 or higher
- pip package manager
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Clone this repository:
git clone https://github.com/makiatulmsyr17/decai-prognosis.git cd decai-prognosis -
Install the required dependencies:
pip install -r requirements.txt
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Run the application:
streamlit run app.py
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Open your web browser and navigate to
http://localhost:8501.
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Enter the required health parameters in the input fields:
- Age
- Pulse Pressure
- Systolic and Diastolic Blood Pressure
- Serum Cholesterol
- Sedimentation Rate
- Gender
- Serum Albumin
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Click the Predict button to see the result.
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To reset all inputs, click the 🔄 button.
The predictive model used in this application is trained on a dataset of health indicators. It employs a supervised learning algorithm to classify individuals into two categories:
- At Risk of Mortality
- Not at Risk of Mortality
We welcome contributions to improve DecAI Prognosis. Please fork the repository, create a new branch for your feature, and submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
For questions or feedback, please contact:
- Developer: Makiatul Musyaropah
- Email: [email protected]