Comparison and use of explainable machine learning-based survival models for heart failure patients
Objective Explainable machine learning (XAI) was introduced in this study to improve the interpretability, explainability and transparency of the modelling results. The survex package in R was used to interpret and compare two survival models – the Cox proportional hazards regression (coxph) model a...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-08-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241277027 |