ArticleComputational intelligence and neuroscience2022
Injuries in College Basketball Sports Based on Machine Learning from the Perspective of the Integration of Sports and Medicine.
Article in Computational intelligence and neuroscience, 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 It has been retracted, and should not be counted.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.
- Retracted: Injuries in College Basketball Sports Based on Machine Learning from the Perspective of the Integration of Sports and Medicine.Computational intelligence and neuroscience · 2023Article
- Segmental Bioimpedance Analysis as a Predictor of Injury and Performance Status in Professional Basketball Players: A New Application Potential?Life (Basel, Switzerland) · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students' basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students.
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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.