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Classification of Hospital of the Future Applications using Machine Learning

Effective health management is critical to ensure patients have access to necessary healthcare services. There are a number of challenges that can limit the provision of medical treatment, including a shortage of healthcare professionals, limited resources, and geographical barriers. Hospital of the...

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Main Authors: Zulkifli I.T., Radzi N.A.M., Aripin N.M., Azmi K.H.M., Samidi F.S., Azhar N.A.
Other Authors: 57982505900
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
5G
Deep Learning
Hospital of the Future
Machine Learning
Network slicing
5G mobile communication systems
Deep learning
Learning systems
Supervised learning
5g
Future applications
Health management
Healthcare services
Hospital of the future
Machine learning models
Machine-learning
Management IS
Hospitals
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id my.uniten.dspace-34551
record_format dspace
spelling my.uniten.dspace-345512024-10-14T11:20:36Z Classification of Hospital of the Future Applications using Machine Learning Zulkifli I.T. Radzi N.A.M. Aripin N.M. Azmi K.H.M. Samidi F.S. Azhar N.A. 57982505900 57218936786 35092180800 57982272200 57215054855 57219033091 5G Deep Learning Hospital of the Future Machine Learning Network slicing 5G mobile communication systems Deep learning Learning systems Supervised learning 5g Deep learning Future applications Health management Healthcare services Hospital of the future Machine learning models Machine-learning Management IS Network slicing Hospitals Effective health management is critical to ensure patients have access to necessary healthcare services. There are a number of challenges that can limit the provision of medical treatment, including a shortage of healthcare professionals, limited resources, and geographical barriers. Hospital of the Future (HoF) incorporates a number of technologies and innovations to improve the delivery of healthcare services and support effective health management. 5G network slicing has the potential to greatly enhance the capabilities of hospitals and the delivery of healthcare services. The network can be sliced into three main services eMBB, mMTC, and URLLC. This paper presented a comparison of various supervised machine learning models in predicting the three network services. The classification for the slices is based on HoF applications' requirements. Deep learning model has the highest accuracy of 100% with total runtime of 85.7s and lowest standard deviation value. In comparison with other machine learning models, deep learning is the best model in predicting 5GHoF slices. � 2023 IEEE. Final 2024-10-14T03:20:36Z 2024-10-14T03:20:36Z 2023 Conference Paper 10.1109/ISCAIE57739.2023.10165466 2-s2.0-85165172183 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165172183&doi=10.1109%2fISCAIE57739.2023.10165466&partnerID=40&md5=97c3301aea6094e345d669dd99224bbf https://irepository.uniten.edu.my/handle/123456789/34551 13 17 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic 5G
Deep Learning
Hospital of the Future
Machine Learning
Network slicing
5G mobile communication systems
Deep learning
Learning systems
Supervised learning
5g
Deep learning
Future applications
Health management
Healthcare services
Hospital of the future
Machine learning models
Machine-learning
Management IS
Network slicing
Hospitals
spellingShingle 5G
Deep Learning
Hospital of the Future
Machine Learning
Network slicing
5G mobile communication systems
Deep learning
Learning systems
Supervised learning
5g
Deep learning
Future applications
Health management
Healthcare services
Hospital of the future
Machine learning models
Machine-learning
Management IS
Network slicing
Hospitals
Zulkifli I.T.
Radzi N.A.M.
Aripin N.M.
Azmi K.H.M.
Samidi F.S.
Azhar N.A.
Classification of Hospital of the Future Applications using Machine Learning
description Effective health management is critical to ensure patients have access to necessary healthcare services. There are a number of challenges that can limit the provision of medical treatment, including a shortage of healthcare professionals, limited resources, and geographical barriers. Hospital of the Future (HoF) incorporates a number of technologies and innovations to improve the delivery of healthcare services and support effective health management. 5G network slicing has the potential to greatly enhance the capabilities of hospitals and the delivery of healthcare services. The network can be sliced into three main services
author2 57982505900
author_facet 57982505900
Zulkifli I.T.
Radzi N.A.M.
Aripin N.M.
Azmi K.H.M.
Samidi F.S.
Azhar N.A.
format Conference Paper
author Zulkifli I.T.
Radzi N.A.M.
Aripin N.M.
Azmi K.H.M.
Samidi F.S.
Azhar N.A.
author_sort Zulkifli I.T.
title Classification of Hospital of the Future Applications using Machine Learning
title_short Classification of Hospital of the Future Applications using Machine Learning
title_full Classification of Hospital of the Future Applications using Machine Learning
title_fullStr Classification of Hospital of the Future Applications using Machine Learning
title_full_unstemmed Classification of Hospital of the Future Applications using Machine Learning
title_sort classification of hospital of the future applications using machine learning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061061189402624
score 13.214268

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