Healthcare AI & Machine Learning
Bayesian-Optimized Explainable AI for CKD Risk Stratification: A Dual-Validated Framework
This research introduces an integrated framework for Chronic Kidney Disease (CKD) risk stratification, combining XGBoost with Optuna-driven Bayesian optimization. Evaluated against 19 competing hyperparameter tuning approaches and validated using dual-paradigm statistics, the model achieves 93.43% accuracy, 93.13% F1-score, and 97.59% ROC-AUC. Key contributions include significant F1-score and ROC-AUC gains over baselines, drastic reduction in hyperparameter tuning trials (50 vs. 540 for grid search), and 54.2% dimensionality reduction through Boruta feature selection. Four explainability techniques consistently identified CKD stage and albumin-creatinine ratio as principal predictors, aligning with KDIGO clinical guidelines. Clinical utility evaluation showed 98.4% positive case detection at a 50% screening threshold and near-optimal calibration, with structural equation modeling pinpointing hyperuricemia as the most potent modifiable risk factor. This framework supports evidence-informed screening protocols by delivering precise, interpretable, and clinically aligned CKD risk stratification.
Executive Impact: Key Performance Indicators
The proposed AI framework demonstrates significant advancements in critical metrics for CKD risk stratification, setting new benchmarks for accuracy, efficiency, and clinical interpretability.
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Accuracy
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F1-Score
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Dimensionality Reduction
Deep Analysis & Enterprise Applications
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6.22%
F1-score improvement over conventional XGBoost
| Model | F1-score | ROC-AUC | Key Advantage |
|---|---|---|---|
| Ours (Optimized XGBoost) | 93.13% | 97.59% | Bayesian optimization, multi-method interpretability |
| XGBoost (Baseline) | 86.91% | 95.68% | Strong ensemble, but manual tuning needed |
| LightGBM (Baseline) | 86.14% | 95.82% | Fast training, but manual tuning needed |
| CatBoost (Baseline) | 84.52% | 96.30% | Handles categorical features, but manual tuning needed |
50 Trials
Required for TPE optimization vs. 540 for Grid Search
| Method | Trials Needed | Achieved F1-Score |
|---|---|---|
| Ours (Optuna TPE) | 50 | 93.13% |
| Grid Search | 540 | 90.20% |
| FLAML | 1069 | 90.15% |
| H2O AutoML | 16 | 89.63% (but lower accuracy) |
Enterprise Process Flow
SHAP
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LIME
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ALE
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Eli5
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Convergent Feature Identification
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CKD Stage & ACR as Principal Predictors
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Alignment with KDIGO Guidelines
Clinical Guideline Concordance
The framework's interpretability techniques (SHAP, LIME, ALE, Eli5) consistently identified CKD stage and albumin-creatinine ratio (ACR) as the most significant predictors for CKD risk. This direct alignment with the Kidney Disease: Improving Global Outcomes (KDIGO) clinical guidelines reinforces the model's reliability and clinical utility.
Key Finding: Consistent identification of CKD stage and ACR as principal predictors validates the model's clinical relevance and facilitates trust among healthcare professionals. The structural equation modeling further revealed hyperuricemia as a potent modifiable risk factor (β = -3.19, p < 0.01), opening new avenues for targeted interventions.
-1.13%
Generalization Gap (indicating robust stability)
| Metric | Ours | XGBoost Baseline | GaussianNB (Worst) |
|---|---|---|---|
| Cross-Validation Std Dev | 0.0121 | 0.0130 | 0.0509 |
| Generalization Gap | -1.13% | -6.09% | 1.31% |
| Effect Size (vs. Baselines) | 0.665-5.433 (Strong) | N/A | 5.653 (Very Strong Underperformance) |
98.4%
Positive Case Detection at 50% Screening Threshold
Enhanced Patient Screening Protocols
Beyond raw performance metrics, the framework's clinical utility was rigorously assessed. At a 50% screening threshold, the model achieved 98.4% positive case detection, significantly outperforming random selection baselines and demonstrating a twofold efficiency gain for targeted intervention programs. This translates to earlier identification of at-risk patients, enabling timely interventions and potentially decelerating disease progression.
Key Finding: The high positive case detection rate and efficiency gains support the integration of this AI model into evidence-informed screening protocols, optimizing resource allocation and improving patient outcomes. The near-optimal calibration (MAE: 0.138) ensures reliable probability estimates for therapeutic planning.
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Estimated Annual Savings
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Implementation Roadmap
A structured approach to integrating Bayesian-Optimized Explainable AI into your clinical workflows, ensuring robust, ethical, and effective deployment.
Phase 1: Data Integration & Preprocessing
Consolidate heterogeneous clinical data from diverse sources, perform systematic cleaning, imputation, outlier detection, and standardization.
Phase 2: Feature Engineering & Selection
Apply encoding transformations, Boruta-based variable selection to reduce dimensionality by 54.2%, and engineered feature combinations.
Phase 3: Model Development & Optimization
Construct XGBoost classifier, optimize hyperparameters using Optuna's Tree-structured Parzen Estimator (50 trials), and implement 5-fold cross-validation.
Phase 4: Dual-Paradigm Statistical Validation
Rigorously assess model generalization and stability using both frequentist (p-values, CIs) and Bayesian (Bayes factors, posterior probabilities) methods across 30 replications.
Phase 5: Explainability & Clinical Alignment
Apply SHAP, LIME, ALE, and Eli5 for feature contribution analysis, ensuring concordance with KDIGO guidelines (CKD stage, ACR).
Phase 6: Clinical Utility Assessment & Deployment Strategy
Evaluate positive case detection, calibration, decision curves, and structural equation modeling (hyperuricemia), then formulate actionable screening protocols.
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