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Predictive models for diabetes mellitus using machine learning techniques

Overview of attention for article published in BMC Endocrine Disorders, October 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

policy
1 policy source
patent
2 patents

Citations

dimensions_citation
173 Dimensions

Readers on

mendeley
289 Mendeley
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Title
Predictive models for diabetes mellitus using machine learning techniques
Published in
BMC Endocrine Disorders, October 2019
DOI 10.1186/s12902-019-0436-6
Pubmed ID
Authors

Hang Lai, Huaxiong Huang, Karim Keshavjee, Aziz Guergachi, Xin Gao

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 289 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 289 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 30 10%
Student > Ph. D. Student 28 10%
Student > Bachelor 23 8%
Researcher 22 8%
Unspecified 10 3%
Other 37 13%
Unknown 139 48%
Readers by discipline Count As %
Computer Science 52 18%
Medicine and Dentistry 20 7%
Engineering 19 7%
Unspecified 10 3%
Nursing and Health Professions 8 3%
Other 30 10%
Unknown 150 52%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2022.
All research outputs
#3,430,945
of 23,630,563 outputs
Outputs from BMC Endocrine Disorders
#107
of 788 outputs
Outputs of similar age
#72,475
of 355,869 outputs
Outputs of similar age from BMC Endocrine Disorders
#3
of 21 outputs
Altmetric has tracked 23,630,563 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 788 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 355,869 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.