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A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Overview of attention for article published in Frontiers in Genetics, March 2023
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1 X user

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4 Mendeley
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Title
A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network
Published in
Frontiers in Genetics, March 2023
DOI 10.3389/fgene.2023.1094099
Pubmed ID
Authors

Qingqing Zhu, Jie Liu

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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 25%
Student > Bachelor 1 25%
Researcher 1 25%
Lecturer 1 25%
Readers by discipline Count As %
Unspecified 1 25%
Biochemistry, Genetics and Molecular Biology 1 25%
Computer Science 1 25%
Chemistry 1 25%
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 09 March 2023.
All research outputs
#20,884,375
of 23,505,669 outputs
Outputs from Frontiers in Genetics
#9,004
of 12,528 outputs
Outputs of similar age
#266,394
of 339,524 outputs
Outputs of similar age from Frontiers in Genetics
#243
of 505 outputs
Altmetric has tracked 23,505,669 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,528 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% 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 339,524 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 505 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.