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Mendeley readers
| Title |
On the interpretability of machine learning-based model for predicting hypertension
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2019
|
| DOI | 10.1186/s12911-019-0874-0 |
| Pubmed ID | |
| Authors |
Radwa Elshawi, Mouaz H. Al-Mallah, Sherif Sakr |
Mendeley readers
The data shown below were compiled from readership statistics for 226 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 226 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 38 | 17% |
| Student > Ph. D. Student | 33 | 15% |
| Researcher | 20 | 9% |
| Student > Bachelor | 15 | 7% |
| Student > Doctoral Student | 14 | 6% |
| Other | 22 | 10% |
| Unknown | 84 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 47 | 21% |
| Medicine and Dentistry | 18 | 8% |
| Engineering | 16 | 7% |
| Social Sciences | 6 | 3% |
| Nursing and Health Professions | 5 | 2% |
| Other | 40 | 18% |
| Unknown | 94 | 42% |