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System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm
The exercise volume and exercise level can be quantitatively assessed by measuring and collecting athletes' health and exercise data. The protection of athletes' health information has lately become an important research topic due to a rise in sports activities. However, due to the nature...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534660/ https://www.ncbi.nlm.nih.gov/pubmed/36212717 http://dx.doi.org/10.1155/2022/5989819 |
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author | Liu, Long Fan, Xiaodong |
author_facet | Liu, Long Fan, Xiaodong |
author_sort | Liu, Long |
collection | PubMed |
description | The exercise volume and exercise level can be quantitatively assessed by measuring and collecting athletes' health and exercise data. The protection of athletes' health information has lately become an important research topic due to a rise in sports activities. However, due to the nature of the data and the limits of protection models, protecting athlete health data is a complex undertaking. Machine learning and blockchain have caused worldwide technological innovation, and it is bound to bring deep modifications to the sports industry. The main purpose of blockchain is security, decentralization, traceability, and credibility of the athlete's health data protection and gathering system. To progress and increase the sports industry and methodically assess the physical fitness of sportspersons' health information, this study concentrates on the Machine Learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) proposed in the sports industry. The ML technique is utilized to clean and handle the information to comprehend the recognition and secure managing of the sportsperson's fitness information. The system uses attribute-based access control, which permits dynamic and fine-grained access to athlete health data, and then stores the health data in the blockchain, which can be secured and tamper-proof by expressing the respective smart contracts. The simulation outcomes illustrate that the suggested MLB-AHIPS attains a high accuracy ratio of 97.8%, security ratio of 98.3%, an efficiency ratio of 97.1%, scalability ratio of 98.9%, and data access rate of 97.2% compared to other existing approach. |
format | Online Article Text |
id | pubmed-9534660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95346602022-10-06 System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm Liu, Long Fan, Xiaodong Biomed Res Int Research Article The exercise volume and exercise level can be quantitatively assessed by measuring and collecting athletes' health and exercise data. The protection of athletes' health information has lately become an important research topic due to a rise in sports activities. However, due to the nature of the data and the limits of protection models, protecting athlete health data is a complex undertaking. Machine learning and blockchain have caused worldwide technological innovation, and it is bound to bring deep modifications to the sports industry. The main purpose of blockchain is security, decentralization, traceability, and credibility of the athlete's health data protection and gathering system. To progress and increase the sports industry and methodically assess the physical fitness of sportspersons' health information, this study concentrates on the Machine Learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) proposed in the sports industry. The ML technique is utilized to clean and handle the information to comprehend the recognition and secure managing of the sportsperson's fitness information. The system uses attribute-based access control, which permits dynamic and fine-grained access to athlete health data, and then stores the health data in the blockchain, which can be secured and tamper-proof by expressing the respective smart contracts. The simulation outcomes illustrate that the suggested MLB-AHIPS attains a high accuracy ratio of 97.8%, security ratio of 98.3%, an efficiency ratio of 97.1%, scalability ratio of 98.9%, and data access rate of 97.2% compared to other existing approach. Hindawi 2022-09-28 /pmc/articles/PMC9534660/ /pubmed/36212717 http://dx.doi.org/10.1155/2022/5989819 Text en Copyright © 2022 Long Liu and Xiaodong Fan. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Long Fan, Xiaodong System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm |
title | System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm |
title_full | System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm |
title_fullStr | System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm |
title_full_unstemmed | System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm |
title_short | System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm |
title_sort | system construction of athlete health information protection based on machine learning algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534660/ https://www.ncbi.nlm.nih.gov/pubmed/36212717 http://dx.doi.org/10.1155/2022/5989819 |
work_keys_str_mv | AT liulong systemconstructionofathletehealthinformationprotectionbasedonmachinelearningalgorithm AT fanxiaodong systemconstructionofathletehealthinformationprotectionbasedonmachinelearningalgorithm |