Multivariate random forest prediction of poverty and malnutrition prevalence.
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models often rely on proprietary data and/or deep or tra...
Saved in:
Main Authors: | Chris Browne, David S Matteson, Linden McBride, Leiqiu Hu, Yanyan Liu, Ying Sun, Jiaming Wen, Christopher B Barrett |
---|---|
Format: | article |
Language: | EN |
Published: |
Public Library of Science (PLoS)
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/e90e4f8fad114eea9a676e4cc99a8b9b |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Poverty and childhood malnutrition: Evidence-based on a nationally representative survey of Bangladesh.
by: Md Ashfikur Rahman, et al.
Published: (2021) -
Prevalence and Associated Factors of Coexistence of Malnutrition and Sarcopenia in Geriatric Rehabilitation
by: Shinta Nishioka, et al.
Published: (2021) -
The prevalence and consequences of malnutrition risk in elderly Albanian intensive care unit patients
by: Shpata V, et al.
Published: (2015) -
Prevalence and prognostic significance of malnutrition in diabetic patients with coronary artery disease: a cohort study
by: Wen Wei, et al.
Published: (2021) -
Quantifying old-growthness of lowland European beech forests by a multivariate indicator for forest structure
by: Peter Meyer, et al.
Published: (2021)