Integrating Innovation in Healthcare: The Evolution of 'CURA's' AI-Driven Virtual Wards for Enhanced Diabetes and Kidney Disease Monitoring

Abstract

The healthcare sector faces intricate challenges that demand innovative solutions to enhance patient outcomes and streamline operations. The advent of Artificial Intelligence (AI) has unleashed groundbreaking potential in numerous healthcare domains, including diagnostics, patient care, and disease management. This study explores the incorporation of AI-driven methodologies for the advanced monitoring of diabetes and kidney diseases. It underscores the development of predictive models that utilize six Machine Learning (ML) and four deep learning (DL) models: Our comprehensive data analysis and rigorous model evaluation showcase AI's capability to significantly enhance clinical practices, fostering a proactive healthcare environment marked by precision, personalization, and predictive care. Our results demonstrate substantial enhancements in the accuracy of disease monitoring. For diabetes prediction, the Gradient Boosting (GB) and Random Forest (RF) models achieved up to 89.61% accuracy, while the hybrid LSTM-CNN model outperformed other DL models with an accuracy of 89.7%. For kidney disease prediction, the RF model reached 97.5% accuracy, and the LSTM-CNN model demonstrated a remarkable accuracy of 98.9%. These findings underscore the transformative potential of AI in healthcare, fostering a proactive environment characterized by precision, personalization, and predictive care. Integrating AI within CURA's virtual wards facilitates earlier disease detection and timely interventions and enables more tailored treatment plans, ultimately optimizing healthcare delivery and patient management.