Angwach Abrham Asnake on Muck Rack

Angwach Abrham Asnake

As seen in: Nature, Wiley Online Library, PLOS, PLOS ONE, BMJ Open, Flipboard
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Angwach Abrham

Contact Angwach Abrham, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Predicting household cooking fuel choice in sub-Saharan Africa using supervised machine learning analysis of DHS data from 28 countries

|
By Lidetu Demoze, Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Gelila Yitageasu, Natnael Gizachew
| Nature
Abstract Approximately 85% of the population in sub-Saharan Africa equating to around 894 million people depend on traditional biomass fuels such as firewood, charcoal, and agricultural waste for their cooking needs. Understanding what shapes cooking fuel choice in sub-Saharan Africa is essential for supporting clean energy transitions and advancing national policies and global goals, including Sustainable Development Goals (SDGs) 3, 5, 7, and 13.
Open in Who Shared Issue with article?

Machine learning based classification of female genital mutilation in 11 Sub-Saharan African countries using demographic and health survey data - Scientific Reports

|
By Alemayehu Kasu Gebrehana, Lidetu Demoze, Gelila Yitageasu, Bizunesh Fantahun Kase, Mitkie Tigabie, Angwach Abrham Asnake
| Nature
Abstract Female Genital mutilation (FGM) has been associated with numerous negative health effects like sexual dysfunction, chronic pain, infections, infertility, and an increased risk of maternal mortality. To date, no recognized studies in sub-Saharan Africa (SSA) have combined machine learning (ML) models with nationally representative Demographic and Health Survey (DHS) data to classify FGM. Therefore, this study aims to identify the most effective ML model for classifying FGM in SSA.
Open in Who Shared Issue with article?

Prediction of under-five mortality using supervised machine learning algorithms in the 23 sub-Sharan African countries - Scientific Reports Original

|
By Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Zufan Alamrie Asmare, Beminate Lemma Seifu, Hiwot Altaye Asebe, Meklit Melaku Bezie, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Mamaru Melkam, Bezawit Melak Fente
| Nature
Abstract Under-five mortality remains a critical public health concern in Sub-Saharan Africa (SSA), ranges from 59 to 230 per 1000 live births in different nations of SSA. Despite substantial efforts to reduce child mortality, progress in many SSA countries has been uneven and slow. Existing predictive models often fall short due to small sample sizes, and a lack of interpretability.
Open in Who Shared View Duplicates (2) Issue with article?
See all 9 articles

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Get in touch with Angwach Abrham

Contact Angwach Abrham, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack
 •  Privacy  •  Terms  •  Security and Legal  •   •   •   • 

© 2026 Muck Rack

SOC 2® Certified