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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-223 |
| Pubmed ID | |
| Authors |
Rajkumar Palaniappan, Kenneth Sundaraj, Sebastian Sundaraj |
| Abstract |
Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Japan | 1 | <1% |
| Turkey | 1 | <1% |
| Unknown | 154 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 19% |
| Student > Master | 24 | 15% |
| Researcher | 19 | 12% |
| Student > Bachelor | 17 | 11% |
| Lecturer | 10 | 6% |
| Other | 22 | 14% |
| Unknown | 36 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 51 | 32% |
| Computer Science | 21 | 13% |
| Medicine and Dentistry | 9 | 6% |
| Physics and Astronomy | 4 | 3% |
| Nursing and Health Professions | 3 | 2% |
| Other | 20 | 13% |
| Unknown | 50 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 January 2019.
All research outputs
#6,272,272
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#2,395
of 7,272 outputs
Outputs of similar age
#59,647
of 227,675 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 153 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 227,675 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 153 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 69% of its contemporaries.