AIMC Topic: Socioeconomic Factors

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Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

PloS one
BACKGROUND: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the availa...

What factors influence the willingness and intensity of regular mobile physical activity?- A machine learning analysis based on a sample of 290 cities in China.

Frontiers in public health
INTRODUCTION: This study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-...

The influence of factors related to public health campaigns on vaccination behavior among population of Wuxi region, China.

Frontiers in public health
BACKGROUND: Public health campaigns are essential for promoting vaccination behavior, but factors such as socioeconomic status, geographical location, campaign quality, and service accessibility influence vaccine uptake. In the Wuxi region of China, ...

Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach.

PloS one
This study conducts a comprehensive analysis of gender inequality in Sri Lanka, focusing on the relationship between key socioeconomic factors and the Gender Inequality Index (GII) from 1990 to 2022. By applying machine learning techniques, including...

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.)
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 ...

Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.

BMC public health
BACKGROUND: Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to scree...

Novel concept for the healthy population influencing factors.

Frontiers in public health
In the rapid urbanization process in China, due to reasons such as employment, education, and family reunification, the number of mobile population without registered residence in the local area has increased significantly. By 2020, the group had a p...

Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol.

Nutrients
BACKGROUND/OBJECTIVES: Child acute malnutrition is a global public health problem, affecting 45 million children under 5 years of age. The World Health Organization recommends monitoring weight gain weekly as an indicator of the correct treatment. Ho...

Machine learning analysis of the effects of COVID-19 on migration patterns.

Scientific reports
This study investigates the impact of the COVID-19 pandemic on European tourist mobility patterns from 2019 to 2021 by conceptualizing countries as monomers emitting radiation to model and analyze their patterns through the lens of socio-economics an...