Multi-Level Health Knowledge Mining Process in P2P Edge Network

Abstract

Chronic diseases are increasing due to westernized eating habits and everyday life changes, and healthcare and disease prevention should be managed based on constant interest. Users, who are not health professionals, have difficulty in obtaining accurate information related to healthcare due to noise problems such as subjective opinions, distorted information, and exaggerated information. There is a need for a method that enables users to obtain meaningful information for healthcare and disease prevention in real-time among the vast amounts of data collected through search. In this study, we propose a multi-level health knowledge mining process in a P2P edge network. The proposed method suggests a P2P edge network to solve the overload problem of P2P networking, the noise problem, and the security problem of cloud computing and mines the health knowledge through the mutual information according to the association rules. In addition, the results of health knowledge mining are visualized to propose a method by which users can easily receive relevant health information. As a result of the performance evaluation, the F-measure using recall and precision is 83%, 79%, 75%, 74%, and 73% of the support ratings of 10%, 20%, 30%, 40%, and 50% Was derived. Accordingly, it is possible to process and analyze healthcare-related information in realtime through a multi-level based health knowledge tree based on the association of data collected by P2P edge computing. In addition, by visualizing meaningful information to the user through the embedding network structure, it provides personalized information for intuitive understanding.

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

“…The first dimension is support human, which accounts for 18.920% of the observed total variance, and it contains seven items: transfer knowledge via iots application, sharing knowledge through cloud platforms, support better decision-making, decision support system, educational tool (3D printing), complex geometric shapes and reduce product time. These findings were supported by the research of Lazrak et al (2020), Masuda et al (2021), and Baek and Chung (2021), who designed technology to facilitate of people's operations such as share knowledge between people, support decision-making processes and provide educational tools. The second dimension is labelled as automation (18.791% of variance) and contains eight items: auto-decision of a machine, knowledge extraction from IoT devices, cognitive knowledge of IoT devices, robots connected by cloud computing, artificial intelligence techniques, additive manufacturing robots, controlling IoT devices and promoting innovation.…”

“…The diversity of this knowledge represents the full importance of applying knowledge to the cloud computing. Baek and Chung (2021) proposed a peer-to-peer (P2P) edge network to address disruptive news, P2P network overloads, cloud computing security issues and mining knowledge of health through information sharing. They focused on health problems that arise in their consumption.…”

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare

“…Apriori Algorithm Association rules, generated from common item sets, operate on transactional databases, assessing the strength of relationships between two objects. Utilizing the Apriori algorithm and multi-level association rules, it identifies associations in health transactions, although it is time-consuming [36]. For COVID-19 prediction, Shaikh and Chitre proposed a novel Apriori algorithm based on Association Rule Mining (ARM) [37].…”