Welcome to the Diabetes Patient Readmission Prediction repository! This project is aimed at developing a machine-learning model to predict whether a diabetes patient is likely to be readmitted to the hospital within 30 days. Predicting readmission can help healthcare providers better allocate resources and improve patient care.
Diabetes is a chronic condition that requires continuous monitoring and management. Hospital readmissions for diabetic patients can be costly and stressful for both patients and healthcare providers. Predicting readmission risk can enable healthcare professionals to take proactive measures to prevent readmission, improve patient outcomes, and reduce healthcare costs.
To get started with this project, you need to clone the repository to your local machine. Use the following command to clone the repository:
git clone https://github.com/LucienCastle/diabetes-patient-readmission-prediction.gitNext, navigate to the project directory:
cd diabetes-patient-readmission-predictionTo set up the required Python environment, you can use a virtual environment. Create a virtual environment and install the dependencies listed in the requirements.txt file:
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
pip install -r requirements.txtOnce you have installed the required dependencies, you can use the provided Jupyter notebooks to explore the project:
readmission-prediction.ipynb: Explore and preprocess the dataset and Train and evaluate machine learning models for readmission prediction.readmission-prediction-AutoML.ipynb: Train and evaluate ML models using AutoML tools such as auto-sklearn and H2O. Make sure to run and generate cleaned data using the previous notebook.
You can run these notebooks step by step to understand and interact with the project.
H2O is an open-source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data and provides easy productionalization of those models in an enterprise environment.
- Distributed and parallel computing to handle big data.
- Easy-to-use high-level API for users of all skill levels.
- Supports common machine learning algorithms such as generalized linear models, gradient boosting machines, random forests, deep learning, and more.
- Provides a web-based flow UI for building, tuning, and validating models.
For detailed installation instructions and documentation, please visit the H2O Documentation.
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. It is based on Bayesian optimization to find the best machine learning pipeline for a given dataset.
- Automatic model selection and hyperparameter tuning.
- Integration with scikit-learn, making it easy to use and extend.
- Support for regression and classification tasks.
- Ability to specify constraints and custom hyperparameter settings.
The dataset used in this project is available in the uciml directory. It contains patient information, including demographics, medical history, and medications. The target variable is whether a patient was readmitted within 30 days. The dataset is in CSV format.
The predictive model in this project is built using machine learning techniques. We experiment with various algorithms, including logistic regression, decision trees, and random forests, and many more. The trained models are evaluated based on performance metrics such as accuracy, precision, recall, and F1-score.
- Added AutoML functionality
- Change notebook to .py files
- Add CLI functionality
- Deploy on cloud platform GCP/AWS
Disclaimer: This project is for educational and research purposes only. It should not be used as a substitute for medical advice or diagnosis. Always consult with a qualified healthcare professional for medical decisions and treatments.