Evidence map›Paper›PMID 40121333›Full record

ArticleScientific reports2025

The optimization and impact of public sports service quality based on the supervised learning model and artificial intelligence.

Ying Yan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Ying YanCollege of Physical Education, Hunan City University, Yiyang, 413000, Hunan, China. yanying@hncu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework of this study is established. Secondly, the technical framework is constructed based on the supervised learning model. Finally, the comprehensive performance of the model is evaluated using a dataset and practical application. The results show that when the model is used to process public sports service data, its performance is excellent. Specifically, the model's accuracy and recall in processing various types of data markedly exceed expectations, with the accuracy reaching more than 88% and the recall remaining at a similarly high level. This remarkable result not only validates the supervised learning model's practicability in the quality optimization of public sports services but also highlights its huge application potential and value. In addition, the possibility and challenge of the model in practical application are also discussed, which provides a useful reference for further improving the quality of public sports service. The findings of this study enrich the research methods in the field of public sports services and offer a scientific basis for relevant decision-making, which helps promote the continuous optimization and development of public sports services.

Indexed as

Artificial IntelligenceSportsSupervised Machine LearningHumansModels, TheoreticalArtificial intelligencePublic sportsQuality optimizationSports serviceSupervised learning

Identifiers

PMID40121333
PMCPMC11929747

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.