AI-Driven Predictive Modeling for Early Detection and Progression Monitoring of Chronic Kidney Disease Using Multimodal Data

Chronic kidney disease (CKD) is a critical global health issue that can lead to end-stage renal failure if not diagnosed and managed early. Early detection and continuous monitoring of CKD progression are essential for optimizing healthcare resources and improving patient outcomes. This research presents an artificial intelligence-driven predictive model that integrates multimodal data, including genetic profiles, imaging, laboratory results, and clinical records, to enhance CKD diagnosis and progression monitoring. By leveraging advanced machine learning techniques, particularly deep learning models, the system effectively processes and analyzes these diverse data sources. The integration of data fusion, feature extraction, and classification techniques improves the accuracy and reliability of CKD detection. Key findings reveal a notable 12%-15% increase in prediction accuracy compared to traditional approaches, demonstrating the potential of AI in transforming CKD management. The system is designed to assist healthcare providers with timely interventions and personalized treatment plans while offering scalability, flexibility, and ongoing patient monitoring to support clinical decision-making.