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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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Mentioned by

twitter
1 tweeter

Citations

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98 Dimensions

Readers on

mendeley
207 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
Authors

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

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 207 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 207 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 12%
Student > Ph. D. Student 24 12%
Student > Bachelor 21 10%
Researcher 20 10%
Other 6 3%
Other 23 11%
Unknown 88 43%
Readers by discipline Count As %
Computer Science 42 20%
Medicine and Dentistry 18 9%
Engineering 18 9%
Nursing and Health Professions 7 3%
Biochemistry, Genetics and Molecular Biology 5 2%
Other 22 11%
Unknown 95 46%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 16 October 2019.
All research outputs
#12,731,175
of 16,027,561 outputs
Outputs from BMC Endocrine Disorders
#282
of 418 outputs
Outputs of similar age
#199,040
of 273,987 outputs
Outputs of similar age from BMC Endocrine Disorders
#1
of 1 outputs
Altmetric has tracked 16,027,561 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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 273,987 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them