Machine Learning-Based Individualized Survival Prediction Model for Prognosis in Osteosarcoma: Data From the SEER Database
Data preprocessing is handled by R code: data_preprocessing.R, which imports codes for data cleaning from preprocessing.R
Model construction, hyperparameters tuning and evaluation are handled with pysurvival, scikit-learn and lifelines packages: ModelDevelopment-Without_tunning_output.ipynb
Web application based on streamlit package: app.py
The original data read in R code is not provided in this repository and needs to be extracted in the SEER database according to inclusion criteria (AYA site = 4.1 Osteosarcoma)
The data after data preprocessing is provided. To reproduce this study, first run the following codes to install packages:
git clone https://github.com/WHUH-ML/Osteosarcoma.git
pip install -r requirements.txt
Then run ModelDevelopmentWithoutTuningOutput.ipynb in Jupyter Notebook.
Run streamlit run app.py in terminal to open the web application locally.
Paper link(To be updated)