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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, November 2013
|
| DOI | 10.1186/1472-6947-13-124 |
| Pubmed ID | |
| Authors |
Kung-Jeng Wang, Bunjira Makond, Kung-Min Wang |
| Abstract |
Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. |
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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 1% |
| Unknown | 78 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 18% |
| Student > Master | 12 | 15% |
| Lecturer | 9 | 11% |
| Student > Doctoral Student | 7 | 9% |
| Student > Bachelor | 6 | 8% |
| Other | 18 | 23% |
| Unknown | 13 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 41% |
| Medicine and Dentistry | 11 | 14% |
| Business, Management and Accounting | 3 | 4% |
| Engineering | 3 | 4% |
| Nursing and Health Professions | 2 | 3% |
| Other | 12 | 15% |
| Unknown | 16 | 20% |
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 14 November 2013.
All research outputs
#18,353,475
of 22,729,647 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,566
of 1,985 outputs
Outputs of similar age
#159,999
of 215,012 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#45
of 48 outputs
Altmetric has tracked 22,729,647 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 215,012 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 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.