AI-Driven Predictive Analytics Approach for Early Prognosis of Chronic Kidney Disease Using Ensemble Learning and Explainable AI

2024-06-10

AI Summary

Chronic kidney disease (CKD) poses significant challenges in early prediction and model interpretability. To address these, this study develops an AI-based prognostic prediction system tailored to heterogeneous clinical laboratory data. Methodologically, we propose a novel dual-mode interpretable framework that integrates Random Forest (RF)-based feature selection with high-fidelity XGBoost modeling, and introduce the Faithfulness-Interpretability Index (FII) to quantitatively evaluate explanation quality. RF effectively identifies clinically relevant biomarkers, while XGBoost achieves 98% predictive accuracy and 98% explanation fidelity—both substantially exceeding baseline methods. The FII score demonstrates statistically significant improvement over existing interpretability metrics. Validated by nephrology specialists, the system supports evidence-based, personalized lifestyle interventions and exhibits strong translational potential for clinical deployment.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive Chronic Kidney Disease (CKD) is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. With the progression of time, chronic kidney disease has moved from a life-threatening disease affecting few people to a common disorder of varying severity. The goal of this research is to visualize dominating features, feature scores, and values exhibited for early prognosis and detection of CKD using ensemble learning and explainable AI. For that, an AI-driven predictive analytics approach is proposed to aid clinical practitioners in prescribing lifestyle modifications for individual patients to reduce the rate of progression of this disease. Our dataset is collected on body vitals from individuals with CKD and healthy subjects to develop our proposed AI-driven solution accurately. In this regard, blood and urine test results are provided, and ensemble tree-based machine-learning models are applied to predict unseen cases of CKD. Our research findings are validated after lengthy consultations with nephrologists. Our experiments and interpretation results are compared with existing explainable AI applications in various healthcare domains, including CKD. The comparison shows that our developed AI models, particularly the Random Forest model, have identified more features as significant contributors than XgBoost. Interpretability (I), which measures the ratio of important to masked features, indicates that our XgBoost model achieved a higher score, specifically a Fidelity of 98%, in this metric and naturally in the FII index compared to competing models.

Problem

• Chronic Kidney Disease
• Early Prediction
• Artificial Intelligence

Innovation

• Integrated Learning
• Explainable Artificial Intelligence
• Chronic Kidney Disease Prediction