You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems
|
|---|---|
| Published in |
Frontiers in Public Health, May 2022
|
| DOI | 10.3389/fpubh.2022.858282 |
| Pubmed ID | |
| Authors |
Sashikala Mishra, Kailash Shaw, Debahuti Mishra, Shruti Patil, Ketan Kotecha, Satish Kumar, Simi Bajaj |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 9% |
| Unspecified | 2 | 6% |
| Researcher | 2 | 6% |
| Student > Master | 2 | 6% |
| Lecturer | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 22 | 65% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 12% |
| Engineering | 3 | 9% |
| Unspecified | 2 | 6% |
| Decision Sciences | 1 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 23 | 68% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 24 May 2022.
All research outputs
#14,074,932
of 23,838,611 outputs
Outputs from Frontiers in Public Health
#3,541
of 11,534 outputs
Outputs of similar age
#192,755
of 445,535 outputs
Outputs of similar age from Frontiers in Public Health
#259
of 1,108 outputs
Altmetric has tracked 23,838,611 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,534 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 67% 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 445,535 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 54% of its contemporaries.
We're also able to compare this research output to 1,108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.