Logo Lanfrica

A Machine Learning Approach to Identifying Shared Determinants of the Childhood Stunting in Kenya, Tanzania, Zambia and Malawi

Record type:

paper
Creator:
SozNgiLan
Publisher:
Spr
Host:

Licenses

https://www.springernature.com/gp/researchers/text-and-data-mininghttps://www.springernature.com/gp/researchers/text-and-data-mining

Similar

Predicting and identifying determinants of stunting among school-aged children in Ethiopia: a machine learning approach

Abstract Background Childhood stunti

Shared component modelling of early childhood anaemia and malaria in Kenya, Malawi, Tanzania and Uganda

Abstract Background Malaria and anaemia contribute substantially to child morbidity and mortality. I

Determinants of Childhood Vaccination Uptake: A Machine Learning Approach Using a Decision Tree Classifier

This study applies a Decision Tree Classifier to determine the most influential factors affecting ch

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

Abstract Background Undernutrition is the main cause of child death in developing countries. This pa

Mapping Local Variations and the Determinants of Childhood Stunting in Nigeria

Introduction: Understanding the specific geospatial variations in childhood stunting is essential fo

Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors

Background : Childhood anaemia remains a significant public health challenge, par