Framework for Cloud-Centric Internet of Medical Things for Real-time Disease Diagnosis
DOI:
https://doi.org/10.63746/njtd.v22i4.3048Keywords:
Automated Medicine, Cloud computing, Dynamic data streaming, Incremental machine learning, Internet of medical things, Patient-centric healthcare, Real – time data analyticsAbstract
The rapid development in the Internet of Medical Things (IoMT) offers new opportunities for real-time health monitoring; however, several key challenges related to IoMT, such as concept drift, latency, and data privacy issues, hinder their optimal effectiveness. The present work addresses these issues by proposing a cloud-centric IoMT framework incorporating wearable devices, edge computing, and scalable cloud infrastructure that would enable efficient real-time health monitoring and predictive analytics. The framework also supports dynamic data streaming, together with incremental machine learning models such as modified Gaussian Naive Bayes (MGNB) that are capable of adjusting to evolving data distributions and delivers reliable predictions. Benchmarking results are presented to demonstrate that, while GNB showed an accuracy of 100% in a static dataset environment, the modified Naive Bayes (MGNB) achieved 90.3% accuracy in dynamic environments, with an AUC of 0.83. These findings demonstrate the potential of the framework to improve patient-centric healthcare through adaptive, scalable, and low-latency systems by addressing critical limitations in existing IoMT solutions. These findings underscore the framework's potential to support adaptive, scalable, and low-latency healthcare systems, thereby advancing patient-centric care.
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