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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 471 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% |
| Bangladesh | 1 | <1% |
| Germany | 1 | <1% |
| India | 1 | <1% |
| Canada | 1 | <1% |
| Switzerland | 1 | <1% |
| Denmark | 1 | <1% |
| Slovenia | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 457 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 73 | 15% |
| Student > Master | 70 | 15% |
| Researcher | 56 | 12% |
| Student > Bachelor | 47 | 10% |
| Other | 21 | 4% |
| Other | 65 | 14% |
| Unknown | 139 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 100 | 21% |
| Engineering | 51 | 11% |
| Medicine and Dentistry | 42 | 9% |
| Agricultural and Biological Sciences | 29 | 6% |
| Biochemistry, Genetics and Molecular Biology | 17 | 4% |
| Other | 83 | 18% |
| Unknown | 149 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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
#3,966,294
of 22,725,280 outputs
Outputs from BMC Medical Informatics and Decision Making
#345
of 1,982 outputs
Outputs of similar age
#17,211
of 94,186 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 10 outputs
Altmetric has tracked 22,725,280 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% 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 94,186 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 81% of its contemporaries.
We're also able to compare this research output to 10 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