A Hybrid Exogenous Dependence Markov Chain Integrated Artificial Neural Network (EDMC-ANN) Model for Predicting Chronic Kidney Disease Staging

Abstract

Chronic kidney disease (CKD) progression modeling requires frameworks capable of capturing both stochastic stage transitions and the influence of time-varying exogenous factors such as comorbidities and biomarkers. Traditional Markov-based models assume time-homogeneous transition probabilities and cannot dynamically incorporate patient-specific covariates, limiting their clinical utility for personalized prognosis. A novel Hybrid Exogenous Dependence Markov Chain Integrated Artificial Neural Network (EDMC-ANN) was developed, where state-specific neural networks learn non-linear relationships between exogenous variables (age, GFR, creatinine, urea, albumin, hemoglobin, blood pressure, diabetes, hypertension) and transition probabilities between five CKD stages. A statistically validated synthetic dataset of 5,000 patient records was generated following clinically observed correlations and stage-dependent progression patterns (KDIGO 2013 guidelines). Data were partitioned via stratified random sampling into training (60%), validation (20%), and test (20%) sets. The EDMC-ANN architecture comprised five state-specific multi-layer perceptrons (128-64-32 units) with ReLU activation, batch normalization, and dropout (0.3,0.3,0.2). Training employed Adam optimization (lr=0.001) with early stopping and learning rate reduction. Performance was evaluated against traditional Markov-based models (Hidden Markov Models, Multi-State Models, basic Markov chains) using accuracy, F1-score, and 95% bootstrap confidence intervals. The EDMC-ANN achieved 83.4% accuracy (95% CI: 81.2-85.6%) in CKD stage classification, with stage-specific F1-scores of 0.93 (Stage 1), 0.88 (Stage 2), 0.68 (Stage 3), 0.52 (Stage 4), and 0.74 (Stage 5). Traditional Markov-based models failed completely (0% accuracy) due to their inability to incorporate exogenous variable dependencies under identical evaluation protocols. GFR emerged as the dominant predictive feature (permutation importance score: 0.42), confirming clinical validity. The EDMC-ANN framework successfully overcomes fundamental limitations of conventional stochastic models by enabling exogenous-conditioned transition probabilities, achieving clinically meaningful predictive accuracy. The model provides interpretable, stage-specific prognostic insights while identifying critical detection challenges in intermediate disease stages. External validation on real-world longitudinal cohorts is recommended prior to clinical deployment.