ArticleBioMed research international2022
System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Classifying athletes and non-athletes by differences in spontaneous brain activity: a machine learning and fMRI study.Brain structure & function · 2025Article
- Retracted: System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm.BioMed research international · 2023Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.