Abstract:Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Biblio… Show more
“…In this section, we employed Timeline View in CiteSpace software to show the temporal distribution of different keywords in the same cluster. The timeline view focused mainly on sketching the relationships between clusters and the historical span of keywords in a given cluster, thus showing the historical development of different research hotspots . The results are shown in Figure .…”
“…In this section, we employed Timeline View in CiteSpace software to show the temporal distribution of different keywords in the same cluster. The timeline view focused mainly on sketching the relationships between clusters and the historical span of keywords in a given cluster, thus showing the historical development of different research hotspots . The results are shown in Figure .…”
“…The discipline is characterised by the prevalence of terms such as "big data," "health care," and "data analytics," which provide insight into its primary issues and areas of focus [48]. The comprehensive approach to preventing age-related diseases is underscored by the focus on technology, data management, and healthcare practises [49]. The co-word network analysis offers a structural framework that elucidates the interconnections among pivotal phrases.…”
The exponential growth of the elderly population poses considerable obstacles to healthcare systems on a global scale, hence requiring the implementation of inventive strategies to identify and mitigate age-related illnesses at an early stage. The primary objective of this study is to explore the use of big data analytics to improve healthcare practices. Specifically, the emphasis is on identifying possible risk factors and developing proactive treatments for senior citizens. The research technique used in this study is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration of 2020. This approach is utilised to ensure a thorough and transparent review of the relevant literature. Moreover, the use of Rstudio software is prevalent in the field of data processing, statistical analysis, and visualisation. By conducting a comprehensive examination of academic databases and medical literature, this study undertakes an analysis of a collection of pertinent papers to explore the significance of big data analytics in the early diagnosis and prevention of diseases in senior populations. The studies that have been chosen include a wide range of healthcare fields, such as cardiology, neurology, cancer, and geriatrics. This selection aims to provide a thorough comprehension of existing practises and identify any possible areas that may need more attention. The results of this study emphasise the significant impact that big data analytics may have on healthcare for the elderly. Using extensive and varied datasets, sophisticated analytical methodologies such as machine learning algorithms and data mining allow the detection of nuanced patterns and correlations that might function as precursors for age-related ailments.
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