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

TL;DR

This paper presents a novel approach combining machine learning and survival analysis to improve risk stratification in chronic kidney disease, aiming for earlier detection and better management of disease progression.

Contribution

It introduces an integrated modeling framework that combines ML-derived predictors with classical survival models for CKD progression prediction.

Findings

Identified new predictors for CKD progression using ML techniques.

Enhanced risk stratification accuracy by integrating ML features with Cox models.

Validated the approach on clinical data, showing improved early detection capabilities.

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.