Performance Prediction of Listed Companies in Smart Healthcare Industry: Based on Machine Learning Algorithms

With the development of wireless network, communication technology, cloud platform, and Internet of Things (IOT), new technologies are gradually applied to the smart healthcare industry. The COVID-19 outbreak has brought more attention to the development of the emerging industry of smart healthcare....

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Vydáno v:Journal of healthcare engineering Ročník 2022; s. 1 - 7
Hlavní autoři: Dong, Baobao, Wang, Xiangming, Cao, Qi
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Hindawi 07.01.2022
Témata:
Algorithms
COVID-19
Health Care Sector
Humans
Machine Learning
SARS-CoV-2
ISSN:2040-2295, 2040-2309, 2040-2309
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Shrnutí:With the development of wireless network, communication technology, cloud platform, and Internet of Things (IOT), new technologies are gradually applied to the smart healthcare industry. The COVID-19 outbreak has brought more attention to the development of the emerging industry of smart healthcare. However, the development of this industry is restricted by factors such as long construction cycle, large investment in the early stage, and lagging return, and the listed companies also face the problem of financing difficulties. In this study, machine learning algorithm is used to predict performance, which can not only deal with a large amount of data and characteristic variables but also analyse different types of variables and predict their classification, increasing the stability and accuracy of the model and helping to solve the problem of poor performance prediction in the past. After analysing the sample data from 53 listed companies in smart healthcare industry, we argued that the conclusion of this study can not only provide reference for listed companies in smart healthcare industry to formulate their own strategies but also provide shareholders with strategies to avoid risks and help the development of this emerging industry.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Academic Editor: Weiwei Cai
ISSN:2040-2295
2040-2309
2040-2309
DOI:10.1155/2022/8091383