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.

Network Function Virtualization (NFV) is one of the most promising technologies which decouples Network Functions (NFs) from hardware resources to support more flexible network services and network resource allocation. However, these benefits increase the possibility of Service Function Chain (SFC) failures due to hardware failures, software defects and burst traffic, resulting in serious consequences. Unfortunately, existing failure detection methods have several issues, such as simplification of detection functionality, heavy overhead, and low accuracy. This paper introduces a framework FullSight, in which control plane and the programmable data plane can collaboratively detect failure and Deep Learning (DL) based algorithms are adopted for failure detection. FullSight can achieve an all-round perception of the state of the SFC, in which network information is acquired through the data plane, SFC components' message is obtained through the control plane. In addition, a failure detection model based on DL is established. Compared with the state-of-the-art methods, FullSight can support 8 kinds of the fine-grained failure detection. Our comprehensive evaluation of prototypes and simulations shows that FullSight can realize rapid and accurate detection and classification of diversified failures in SFCs. The bandwidth overhead reduces by 57% compared with the existing methods. Additionally, FullSight has a detection accuracy up to 93.5%.