Mohammed Elfaramawi
Biographic Data
| ID | 9068209 |
|---|---|
| NAME | Mohammed Elfaramawi |
| GIVEN NAMES | Mohammed |
| FAMILY NAME | Elfaramawi |
| SIGNATURE | ELFARAMAWI M |
| AFFILIATIONS | University of Arkansas for Medical Sciences |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults
With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose significant public health challenges in Bangladesh and many other low- and middle-income countries. This study used machine learning (ML) approaches to develop predictive models for hypertension and diabetes among Bangladeshi adults. Bangladesh Demographic and Health Survey 2022, a nationally representat…
Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017
BACKGROUND: Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent wit…
No prominent works on this page.
Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017
BACKGROUND: Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent wit…
Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults
With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose significant public health challenges in Bangladesh and many other low- and middle-income countries. This study used machine learning (ML) approaches to develop predictive models for hypertension and diabetes among Bangladeshi adults. Bangladesh Demographic and Health Survey 2022, a nationally representat…
AdaBoost (1 work) · Artificial Intelligence in Healthcare (1 work) · Artificial neural network (1 work) · Biology (1 work) · Blood pressure (1 work) · Boosting (machine learning) (1 work) · Burden of disease (1 work) · Demography (1 work) · Diabetes mellitus (1 work) · Diabetes, Cardiovascular Risks, and Lipoproteins (1 work)