AIMC Topic: Poverty

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Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.

JMIR research protocols Apr 16, 2025
BACKGROUND: Individuals who are socioeconomically disadvantaged have high smoking rates and face barriers to participating in smoking cessation interventions. Computer-tailored health communication, which is focused on finding the most relevant messa...
Male Middle Aged Vulnerable Populations Machine Learning Female Adult Smokers Smoking Cessation Text Messaging Randomized Controlled Trials as Topic Humans Poverty Motivation
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Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.

Nutrition (Burbank, Los Angeles County, Calif.) Dec 24, 2024
OBJECTIVES: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the ...
Humans Machine Learning Female Health Surveys India Socioeconomic Factors Family Characteristics Poverty Infant Growth Disorders Child, Preschool Risk Factors Male
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Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Journal of substance use and addiction treatment Jun 8, 2024
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-leve...
Age Factors Unemployment Substance-Related Disorders United States Socioeconomic Factors Residence Characteristics Poverty Social Determinants of Health Patient Acceptance of Health Care Female Young Adult Adult Male Middle Aged Adolescent Humans Machine Learning Mental Disorders
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Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia.

Scientific reports Mar 12, 2024
Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predic...
Poverty Somalia Machine Learning Cross-Sectional Studies
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The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries.

Social science research Dec 30, 2023
Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theo...
Poverty Financial Management Child Socioeconomic Factors Humans Developing Countries Educational Status
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High-resolution rural poverty mapping in Pakistan with ensemble deep learning.

PloS one Apr 4, 2023
High resolution poverty mapping supports evidence-based policy and research, yet about half of all countries lack the survey data needed to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning t...
Rural Population Family Characteristics Poverty Pakistan Humans Deep Learning
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Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters.

International journal of environmental research and public health Dec 15, 2022
Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction stu...
Humans Machine Learning Adult Transportation Logistic Models Adolescent Poverty Child Travel
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Balancing national economic policy outcomes for sustainable development.

Nature communications Aug 26, 2022
The 2030 Sustainable Development Goals (SDGs) aim at jointly improving economic, social, and environmental outcomes for human prosperity and planetary health. However, designing national economic policies that support advancement across multiple Sust...
Sustainable Development Egypt Poverty Humans Climate Change Artificial Intelligence
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Machine learning and phone data can improve targeting of humanitarian aid.

Nature Mar 16, 2022
The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have distributed ...
Humans Machine Learning Poverty Data Analysis Cell Phone Pandemics Relief Work COVID-19
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Multivariate random forest prediction of poverty and malnutrition prevalence.

PloS one Sep 8, 2021
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 ofte...
Social Problems Humans Poverty Machine Learning Malnutrition Prevalence Multivariate Analysis Developing Countries
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