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Predicting disease risks from highly imbalanced data using random forest

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2011
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
2 news outlets
twitter
2 X users
patent
2 patents

Citations

dimensions_citation
517 Dimensions

Readers on

mendeley
622 Mendeley
citeulike
2 CiteULike
connotea
1 Connotea
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Title
Predicting disease risks from highly imbalanced data using random forest
Published in
BMC Medical Informatics and Decision Making, July 2011
DOI 10.1186/1472-6947-11-51
Pubmed ID
Authors

Mohammed Khalilia, Sounak Chakraborty, Mihail Popescu

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 622 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 <1%
France 2 <1%
Indonesia 1 <1%
Germany 1 <1%
Iran, Islamic Republic of 1 <1%
Canada 1 <1%
Belgium 1 <1%
Slovenia 1 <1%
Unknown 609 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 134 22%
Student > Master 97 16%
Researcher 69 11%
Student > Bachelor 36 6%
Student > Doctoral Student 35 6%
Other 85 14%
Unknown 166 27%
Readers by discipline Count As %
Computer Science 144 23%
Engineering 63 10%
Medicine and Dentistry 40 6%
Agricultural and Biological Sciences 25 4%
Business, Management and Accounting 18 3%
Other 124 20%
Unknown 208 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 07 April 2022.
All research outputs
#1,672,112
of 25,880,948 outputs
Outputs from BMC Medical Informatics and Decision Making
#79
of 2,167 outputs
Outputs of similar age
#7,246
of 131,901 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 13 outputs
Altmetric has tracked 25,880,948 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,167 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 96% 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 131,901 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 13 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 92% of its contemporaries.