A Drift-Oriented Adaptive Framework for Concept Drift Detection in Large-Scale Internet-of-Medical-Things Data Streams

Abstract Background The rise of the Internet-of-Medical-Things (IoMT) and smart devices has led to a substantial increase in extensive data streams in the healthcare domain. The interconnected nature of medical devices introduces dynamic and evolving data patterns. However, this dynamism poses a significant challenge known as Concept Drift, particularly crucial in the medical field. Concept Drift reflects the inherent instability in data patterns over time. In medical applications, this challenge intensifies as sensors must seamlessly transition from general healthcare monitoring to handling critical situations like emergency ICU operations. The complexity deepens due to imbalanced data distributions inherent in e-health scenarios. Methods The study introduces an Adaptive Ensemble Framework (AEF-CDA) designed to detect and adapt to concept drift in large-scale medical data streams from IoMT. The framework incorporates adaptive data preprocessing, a novel drift-centric adaptive feature selection approach, the learning of base models, and the selection of models adapted to concept drift. Additionally, an online ensemble model is integrated to enhance concept drift adaptation. Results The proposed AEF-CDA framework is evaluated using three public IoMT and IoT datasets. The experimental results demonstrate its superiority over contemporary methods, achieving a remarkable accuracy of 99.64% with a precision of 99.39%. These metrics surpass the performance of other approaches in the simulation. Conclusion In conclusion, the research presents a robust solution in the form of the adaptive ensemble framework (AEF-CDA) to effectively address the challenges posed by concept drift in IoMT data streams. The demonstrated high accuracy and precision underscore the framework’s efficacy, highlighting its potential significance in the dynamic landscape of medical data analysis.