Abstract
The Internet of Things (IoT) role is instrumental in the technological advancement of the healthcare industry. Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0. Healthcare IoT systems are the emergence of this foresight. The communication systems between the sensing nodes and the processors; and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies. At present, many new technologies supplement these empowering technologies. So, in this research work, a practical feature extraction and classification technique is suggested for handling data acquisition besides data fusion to enhance treatment-related data. In the initial stage, IoT devices are gathered and pre-processed for fusion processing. Dynamic Bayesian Network is considered an improved balance for tractability, a tool for CDF operations. Improved Principal Component Analysis is deployed for feature extraction along with dimension reduction. Lastly, this data learning is attained through Hybrid Learning Classifier Model for data fusion performance examination. In this research, Deep Belief Neural Network and Support Vector Machine are hybridized for healthcare data prediction. Thus, the suggested system is probably a beneficial decision support tool for multiple data sources prediction and predictive ability enhancement.
Highlights
Many kinds of research have recently been grabbed by many healthcare applications likeHealthcare facilities management, disaster relief management, sports health managing, and homebased care [1]
It is substantiated that the suggested CDFT-Hybrid Learning Classifier Model (HLCM) method can obtain optimal F-measure values, which is higher than the existing classification methods
This study proposes and assesses a Hybrid Learning Classifier Model (HLCM) based Contextaware Data Fusion technique for healthcare applications
Summary
Many kinds of research have recently been grabbed by many healthcare applications like. Healthcare facilities management, disaster relief management, sports health managing, and homebased care [1]. A wide range of edge computing technologies aims at offering facilities to the public using embedded intelligent systems in these systems. An extensive range of computing technologies aims to offer facilities to the public by applying embedded intelligent systems to these situations [2]. Pervasive Healthcare Monitoring System (PHMS) aids in enabling realtime besides uninterrupted monitoring of healthcare by pervasive computing technologies. Automatic identification and treatment facilitate independent living, common wellness, and distant disease management deprived of spatial-temporal limitations [3].
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