Development of a Renal Function Assessment and Monitoring System Using Machine Learning and Cloud Computing
Authors: Y. Zhang, L. Wang, H. Li, J. Chen
Chronic kidney disease (CKD) is a global health burden, and early detection is crucial for effective management. This study presents a novel renal function assessment and monitoring system that integrates machine learning algorithms with cloud computing to enable real-time, non-invasive monitoring of renal function. The system utilizes a multi-modal approach, combining clinical biomarkers, patient demographics, and continuous physiological data from wearable sensors. A gradient boosting machine (GBM) model was trained on a large retrospective cohort (n=12,000) and validated on a prospective cohort (n=1,500), achieving an AUC of 0.94 for detecting early-stage CKD. The system also incorporates a cloud-based dashboard for remote monitoring and alerts, facilitating timely interventions. Key innovations include the use of explainable AI (XAI) to provide interpretable predictions, and a federated learning framework to ensure data privacy. The system demonstrated high accuracy, scalability, and usability in clinical settings, suggesting its potential to transform CKD management by enabling proactive, personalized care.
