This repository contains the code for the paper Survival Analysis with Neural Networks for Chronic Heart Failure Patients.
In this project, we investigated using a range of different neural network models in statistical models to examine associations of covariates from clinical data of an inhomogeneous population of 1,802 chronic heart failure patients in the UK-HEART2 cohort for survival analysis. These neural networks were used within a partial-likelihood acting as a loss function in reimplementing the Cox Proportional-Hazards Optimizer in scikit-survival combined with CoxNNet (Ching et al., 2018) to examine the relationships of covariates and estimate a risk score for a patient, given their covariates.
We successfully reimplemented latent class analysis using Gaussian mixture models with the KMeans algorithm and trained a neural network model for each class. The best of these models achieved a c-statistic score of 0.68 using 4 subgroups compared to 0.84 using latent class regression and 0.68 using standard Cox Proportional-Hazards regression (Mbotwa et al., 2021).
We extensively explored different neural network solutions by modifying the network architecture, parameters such as the learning rate and epochs, and completely different solutions using convolutional neural networks in which we transformed patient data into an image where each pixel represented a covariate.
All code can be accessed via the GitHub repository.
Reproducibility is possible by:
git clone https://github.com/omariosc/survival-analysis
pip install -r requirements.txtThe following interactive Python notebook can be run: code.ipynb.
Alternatively, the notebook and dataset can be imported into Google Colaboratory. If there are issues with installing requirements, then this method should be used. However, it can take a few hours to run the entire notebook.