Chronic kidney disease (CKD) affects over 850 million individuals worldwide, yet conventional risk stratification approaches fail to capture complex disease progression patterns. Current machine learning approaches suffer from inefficient parameter optimization and limited clinical interpretability. We developed an integrated framework combining advanced Bayesian optimization with explainable artificial intelligence for enhanced CKD risk assessment. Our approach employs XGBoost ensemble learning with intelligent parameter optimization through Optuna (a Bayesian optimization framework) and comprehensive interpretability analysis using SHAP (SHapley Additive exPlanations) to explain model predictions. To address algorithmic “black-box” limitations and enhance clinical trustworthiness, we implemented four-tier risk stratification using stratified cross-validation and balanced evaluation metrics that ensure equitable performance across all patient risk categories, preventing bias toward common cases while maintaining sensitivity for high-risk patients. The optimized model achieved exceptional performance with 92.4% accuracy, 91.9% F1-score, and 97.7% ROC-AUC, significantly outperforming 16 baseline algorithms by 7.9–18.9%. Bayesian optimization reduced computational time by 74% compared to traditional grid search while maintaining robust generalization. Model interpretability analysis identified CKD stage, albumin-creatinine ratio, and estimated glomerular filtration rate as primary predictors, fully aligning with established clinical guidelines. This framework delivers superior predictive accuracy while providing transparent, clinically-meaningful explanations for CKD risk stratification, addressing critical challenges in medical AI deployment: computational efficiency, algorithmic transparency, and equitable performance across diverse patient populations.

Chronic kidney disease (CKD) impacts over 850 million people globally, representing a critical public health issue, yet existing risk assessment methodologies inadequately address the complexity of disease progression trajectories. Traditional machine learning approaches encounter critical limitations including inefficient hyperparameter selection and lack of clinical transparency, hindering their deployment in healthcare settings. This study introduces an innovative computational framework that integrates adaptive Multi-Armed Bandit (MAB) strategies with BorderlineSMOTE sampling techniques to improve CKD risk assessment. The proposed methodology leverages XGBoost within an ensemble learning paradigm enhanced by Upper Confidence Bound exploration strategy, coupled with a comprehensive interpretability system incorporating SHAP and LIME analytical tools to ensure model transparency. To address the challenge of algorithmic interpretability while maintaining clinical utility, a four-level risk categorization framework was developed, employing cross-validated stratification methods and balanced performance evaluation metrics, thereby ensuring fair predictive accuracy across diverse patient populations and minimizing bias toward dominant risk categories. Through rigorous empirical evaluation on clinical datasets, we performed extensive comparative analysis against sixteen established algorithms using paired statistical testing with Bonferroni correction. The MAB-optimized framework achieved superior predictive performance with accuracy of 91.8%, F1-score of 91.0%, and ROC-AUC of 97.8%, demonstrating superior performance within the evaluated cohort of reference algorithms (p-value < 0.001). Remarkably, our optimized framework delivered nearly ten-fold computational efficiency gains relative to conventional grid search methods while preserving robust classification performance. Feature importance analysis identified albumin-to-creatinine ratio, eGFR measurements, and CKD staging as dominant prognostic factors, demonstrating concordance with established clinical nephrology practice. This research addresses three core limitations in healthcare artificial intelligence: optimization computational cost, model interpretability, and consistent performance across heterogeneous clinical populations, offering a practical solution for improved CKD risk stratification in clinical practice.

Accurate prediction of ultra-high-performance concrete (UHPC) compressive strength is essential for optimizing mixture design and reducing experimental iterations. Existing machine learning approaches suffer from limited algorithm diversity, insufficient statistical validation, and inadequate uncertainty quantification. This study presents a comprehensive framework through systematic evaluation of 20 algorithms across seven categories on 863 experimental observations. Six physically meaningful composite features (such as water-cement ratio, total binder content, and fiber aspect ratio) are engineered to capture intrinsic material relationships, with the Boruta algorithm employed for feature selection. Statistical robustness is ensured through 30 repeated experiments analyzed using both frequentist (p-value, effect size, 95% CI) and Bayesian approaches. CatBoost achieves optimal performance (R² = 0.8979 ± 0.0239, RMSE = 10.58 ± 1.45 MPa), with curing age, sand content, and steel fiber volume identified as dominant predictors through multi-perspective interpretability analysis integrating SHAP, ALE, permutation importance, and LIME. External validation on 810 independent samples yields R² = 0.5923 (RMSE = 25.68 MPa) under significant cross-dataset conditions, with performance reduction attributed to feature availability differences and distribution shift. Comprehensive uncertainty quantification yields prediction uncertainty of 3.48%, substantially below previously reported thresholds. The proposed framework offers practitioners a reliable tool for UHPC mixture screening while maintaining prediction confidence for structural engineering applications.

1. Fiber-reinforced concrete (FRC) has evolved into a broad family of cementitious composites in which discrete fibers are introduced to improve crack control, post-cracking resistance, toughness, ...

Flexural strength (FS) governs the structural performance of 3D-printed concrete (3DPC) under bending loads yet remains difficult to predict owing to the coupled influence of binder composition, supplementary cementitious materials, water-to-binder ratio, and fiber reinforcement geometry on interlayer fracture behavior. Much of this compositional diversity stems from supplementary cementitious materials, industrial by-products whose reuse as partial cement replacement lowers the embodied carbon of printable mixes. A machine learning framework was trained on 209 FS records covering OPC- and SAC-based systems (FS: 3.65–45.0 MPa; W/B: 0.15–0.65). Six composite features were constructed from physical principles, two of them specific to bending: Fiber_Pullout_Index encoding post-crack pullout energy and Binder_Efficiency capturing cement quality per unit water content at the fiber–matrix interface; Lasso regularization with the one-standard-error rule reduced the 19-variable space to 14 active predictors. Twenty regression algorithms spanning eight families were benchmarked under 30 independent partitions; the Friedman test rejected equal performance (${\chi }^2=337.02$, $p=4.88\times {10}^{-60}$) and all 19 pairwise Wilcoxon comparisons against CatBoost were Holm-significant. CatBoost ranked first (mean rank of 18.17/20; 30-run $R^2=0.9302\pm 0.0722$; seed-42 partition: $R^2=0.9557$; RMSE = 1.761 MPa; MAPE = 11.20%). n(W/B) emerged as the primary driver across SHAP, ALE and LIME, with a monotonic ALE profile spanning 8.99 MPa and no inflection over the full printable window; Binder_Efficiency ranked second (PDP range: 5.21 MPa), isolating cement grade and paste dilution as independent strength levers. Cross-conformal prediction provided finite-sample coverage guarantees without distributional assumptions (empirical coverage: 95.24%; conformity quantile: 3.83 MPa); bootstrap analysis put the epistemic component at a mean predictive SD of 0.946 MPa, a quarter of that quantile. External validation yielded $R^2=0.769$ (Pearson $R=0.923$, RMSE = 1.91 MPa), with 19 of 20 predictions (95.0%) within Bland–Altman 95% limits of agreement, confirming transfer to a source study withheld from model training. A graphical user interface packaging the 14-feature CatBoost pipeline supports mix design queries without programming.