Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk Stratification

TL;DR

A novel approach to modeling CKD progression is proposed using a combination of machine learning techniques and classical statistical models to extract novel predictors for CKD progression, with feature importance assessed using Shapley values.

Abstract

Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associated morbidity, mortality, and financial burden. In this study, we propose a novel approach to modeling CKD progression using a combination of machine learning techniques and classical statistical models. Building on the work of Liu et al. (2023), we evaluate linear models, tree-based methods, and deep learning models to extract novel predictors for CKD progression, with feature importance assessed using Shapley values. These newly identified predictors, integrated with established clinical features from the Kidney Failure Risk Equation, are then applied within the framework of Cox proportional hazards models to predict CKD progression.

Figures (21)

• Figure 1: High-level overview of the model pipeline, including ML feature selection and augmented Cox proportional hazards models.
• Figure 2: Top 40 mean absolute SHAP values from XGboost model.
• Figure 3: Top 40 mean absolute SHAP values from FCNN model.
• Figure 4: Top 40 mean absolute SHAP values from ResNet model.
• Figure 5: Top 40 mean absolute SHAP values from LR model.