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Diabetes classification model based on boosting algorithms

Overview of attention for article published in BMC Bioinformatics, March 2018
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 X user

Citations

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60 Dimensions

Readers on

mendeley
69 Mendeley
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Title
Diabetes classification model based on boosting algorithms
Published in
BMC Bioinformatics, March 2018
DOI 10.1186/s12859-018-2090-9
Pubmed ID
Authors

Peihua Chen, Chuandi Pan

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 14%
Researcher 8 12%
Student > Bachelor 8 12%
Student > Master 5 7%
Lecturer 5 7%
Other 7 10%
Unknown 26 38%
Readers by discipline Count As %
Computer Science 16 23%
Medicine and Dentistry 10 14%
Engineering 3 4%
Mathematics 2 3%
Agricultural and Biological Sciences 2 3%
Other 5 7%
Unknown 31 45%
Attention Score in Context

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 25 April 2018.
All research outputs
#15,633,726
of 23,243,271 outputs
Outputs from BMC Bioinformatics
#5,453
of 7,361 outputs
Outputs of similar age
#211,071
of 330,456 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 113 outputs
Altmetric has tracked 23,243,271 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,361 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 330,456 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.