The INTEGRATION OF ROBOTIC AUTOMATION AND AI FOR IOMT-BASED CHRONIC KIDNEY DISEASE PREDICTION USING TYPE-2 FUZZY LOGIC AND RECURRENT NEURAL NETWORKS (RNN)

IOMT-BASED CHRONIC KIDNEY DISEASE PREDICTION

Abstract

This paper introduces a novel approach for predicting chronic kidney disease (CKD) by integrating advanced technologies, including the Internet of Medical Things (IoMT), robotic automation, and artificial intelligence (AI). The proposed system leverages Recurrent Neural Networks (RNN) to analyze temporal patterns in patient data and employs Type-2 fuzzy logic to handle uncertainties and imprecision in medical data. By incorporating robotic automation, the method enhances data processing efficiency, reducing human error and increasing productivity. The strategy enables real-time monitoring and provides individualized healthcare interventions, offering significant improvements in CKD prediction accuracy and effectiveness. Compared to traditional methods, this approach demonstrates substantial benefits, achieving 94.5% accuracy, 92.3% precision, and a rapid computing time of just 3.2 seconds. The proposed methodology addresses common challenges in medical diagnostics, such as data inconsistency, and holds the potential to transform CKD management by facilitating early detection and personalized treatment strategies.