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Performance Prediction of Listed Companies in Smart Healthcare Industry: Based on Machine Learning Algorithms.
Dong, Baobao; Wang, Xiangming; Cao, Qi.
  • Dong B; School of Management, Jilin University, Changchun 130025, Jilin, China.
  • Wang X; School of Management, Jilin University, Changchun 130025, Jilin, China.
  • Cao Q; School of Management, Jilin University, Changchun 130025, Jilin, China.
J Healthc Eng ; 2022: 8091383, 2022.
Article in English | MEDLINE | ID: covidwho-1622120
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Health Care Sector / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Health Care Sector / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article Affiliation country: 2022