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Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters

Citations

dimensions_citation
193 Dimensions

Readers on

mendeley
308 Mendeley
citeulike
1 CiteULike
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Title
Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
Published in
BMC Medical Informatics and Decision Making, March 2010
DOI 10.1186/1472-6947-10-16
Pubmed ID
Authors

Wei Yu, Tiebin Liu, Rodolfo Valdez, Marta Gwinn, Muin J Khoury

Abstract

We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 308 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 <1%
United Kingdom 2 <1%
Canada 1 <1%
India 1 <1%
Germany 1 <1%
Switzerland 1 <1%
Slovenia 1 <1%
Denmark 1 <1%
Spain 1 <1%
Other 2 <1%
Unknown 294 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 20%
Student > Master 57 19%
Researcher 40 13%
Student > Bachelor 32 10%
Other 16 5%
Other 47 15%
Unknown 55 18%
Readers by discipline Count As %
Computer Science 87 28%
Engineering 36 12%
Agricultural and Biological Sciences 27 9%
Medicine and Dentistry 27 9%
Biochemistry, Genetics and Molecular Biology 15 5%
Other 54 18%
Unknown 62 20%

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 17 September 2020.
All research outputs
#2,682,922
of 17,351,915 outputs
Outputs from BMC Medical Informatics and Decision Making
#241
of 1,572 outputs
Outputs of similar age
#27,483
of 177,043 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 14 outputs
Altmetric has tracked 17,351,915 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 1,572 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 84% 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 177,043 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 84% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.