AIMC Topic: Health Surveys

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Predicting arthritis risk with machine learning: Insights from the 2023 National Health Interview Survey data.

PloS one
Arthritis, a common chronic disease encompassing multiple subtypes of osteoarthritis and rheumatoid arthritis, was explored in this study as a risk-related factor based on data from the 2023 U.S. National Health Interview Survey (NHIS). The study inc...

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data.

PloS one
BACKGROUND AND OBJECTIVE: Bangladesh, a South Asian country, continues to face significant challenges in maternal health, as reflected by its high maternal mortality ratio (MMR). According to the 2022 Bangladesh Demographic and Health Survey (BDHS), ...

An artificial neural network approach for predicting infant mortality status in Ethiopia.

BMC public health
Infant mortality is a major public health issue that is rooted in the larger problem of socio-economic and healthcare disparities. Deep learning techniques were employed in this study to predict infant mortality using data gathered via 2019 Ethiopia ...

Increasing Rigor in Online Health Surveys Through the Reduction of Fraudulent Data.

Journal of medical Internet research
Online surveys have become a key tool of modern health research, offering a fast, cost-effective, and convenient means of data collection. It enables researchers to access diverse populations, such as those underrepresented in traditional studies, an...

Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000-2019).

BMJ open
OBJECTIVE: Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to fun...

Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data.

PloS one
BACKGROUND: Under-5 mortality in Bangladesh remains a critical indicator of public health and socio-economic development. Traditional methods often struggle to capture the complex, non-linear relationships influencing under-5 mortality. This study le...

Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis.

BMC public health
INTRODUCTION: Tobacco use during pregnancy is a significant public health concern, associated with adverse maternal and neonatal outcomes. Despite its critical importance, comprehensive data on tobacco use among pregnant women in sub-Saharan Africa i...

Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.

BMC pediatrics
BACKGROUND: Healthcare practitioners require a robust predictive system to accurately diagnose diseases, especially in young children with conditions such as anemia. Delays in diagnosis and treatment can have severe consequences, potentially leading ...

Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and Health Survey.

Frontiers in public health
INTRODUCTION: Khat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefo...