INTEGRATION OF AI, ML, AND IOT IN HEALTHCARE DATA FUSION: INTEGRATING DATA FROM VARIOUS SOURCES, INCLUDING IOT DEVICES AND ELECTRONIC HEALTH RECORDS, PROVIDES A MORE COMPREHENSIVE VIEW OF PATIENT HEALTH

Abstract

An abundance of new avenues for information sharing have opened up thanks to the IoMT, or Internet of Medical Things. Empowering patients, fostering healthcare collaboration, educating and training medical professionals, utilizing data for innovation, creating personalized treatment plans, managing supply chains, promoting public health, utilizing wearable health devices, and implementing quality improvement initiatives are all possibilities. Concerns about infrastructure costs, data privacy and security, regulations, and interoperability are only a few of the obstacles to the widespread use of IoMT. The purpose of this research is to fill a gap in the literature by discussing the possible solutions to the security issues related to data fusion in IoMT and its ramifications. Prediction accuracy is directly affected by the quantity, quality, and relevance of data acquired from IoMT devices. The most effective algorithm for identifying epileptic seizures in IoMT networks is the Epilepsy seizure detector-based Naive Bayes (ESDNB) algorithm, which achieves an accuracy ranging from 99.53% to 99.99%. On the other hand, data storage needs a complete overhaul, with enhancements needed at every stage (collection, protection, and storage). Possible improvements in the detection of security threats and compromises could result from standardizing architecture and security measures.