scholarly journals Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning

2020 ◽  
Author(s):  
William Ogallo ◽  
Skyler Speakman ◽  
Victor Akinwande ◽  
Kush R. Varshney ◽  
Aisha Walcott-Bryant ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neonatal Mortality ◽  
Neonatal Death ◽  
Household Size ◽  
Sub Saharan Africa ◽  
Age And Gender ◽  
Factors Associated ◽  
Sub Saharan ◽  
And Gender ◽  
Machine Learning Models

AbstractThis study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.

scholarly journals Age and gender trends in insecticide-treated net use in sub-Saharan Africa: a multi-country analysis

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Bolanle Olapeju ◽  
Ifta Choiriyyah ◽  
Matthew Lynch ◽  
Angela Acosta ◽  
Sean Blaufuss ◽  
...  
Keyword(s):  
Sub Saharan Africa ◽  
Age And Gender ◽  
Net Use ◽  
Country Analysis ◽  
Sub Saharan ◽  
And Gender

scholarly journals Decomposing the rural–urban gap in factors associated with childhood immunisation in sub-Saharan Africa: evidence from surveys in 23 countries

2021 ◽  
Vol 6 (1) ◽  
pp. e003773
Author(s):  
Edward Kwabena Ameyaw ◽  
Yusuf Olushola Kareem ◽  
Bright Opoku Ahinkorah ◽  
Abdul-Aziz Seidu ◽  
Sanni Yaya
Keyword(s):  
Decomposition Analysis ◽  
Household Wealth ◽  
Sub Saharan Africa ◽  
Demographic And Health Surveys ◽  
Childhood Immunisation ◽  
Urban Settings ◽  
Factors Associated ◽  
Rural And Urban ◽  
Full Immunisation ◽  
Sub Saharan

BackgroundAbout 31 million children in sub-Saharan Africa (SSA) suffer from immunisation preventable diseases yearly and more than half a million children die because of lack of access to immunisation. Immunisation coverage has stagnated at 72% in SSA over the past 6 years. Due to evidence that full immunisation of children may be determined by place of residence, this study aimed at investigating the rural–urban differential in full childhood immunisation in SSA.MethodsThe data used for this study consisted of 26 241 children pooled from 23 Demographic and Health Surveys conducted between 2010 and 2018 in SSA. We performed a Poisson regression analysis with robust Standard Errors (SEs) to determine the factors associated with full immunisation status for rural and urban children. Likewise, a multivariate decomposition analysis for non-linear response model was used to examine the contribution of the covariates to the observed rural and urban differential in full childhood immunisation. All analyses were performed using Stata software V.15.0 and associations with a p<0.05 were considered statistically significant.ResultsMore than half of children in urban settings were fully immunised (52.8%) while 59.3% of rural residents were not fully immunised. In all, 76.5% of rural–urban variation in full immunisation was attributable to differences in child and maternal characteristics. Household wealth was an important component contributing to the rural–urban gap. Specifically, richest wealth status substantially accounted for immunisation disparity (35.7%). First and sixth birth orders contributed 7.3% and 14.9%, respectively, towards the disparity while 7.9% of the disparity was attributable to distance to health facility.ConclusionThis study has emphasised the rural–urban disparity in childhood immunisation, with children in the urban settings more likely to complete immunisation. Subregional, national and community-level interventions to obviate this disparity should target children in rural settings, those from poor households and women who have difficulties in accessing healthcare facilities due to distance.

scholarly journals Information Technology and Gender Economic Inclusion in Sub-Saharan Africa

2021 ◽  
pp. 1-14
Author(s):  
Simplice A. Asongu ◽  
Joseph Amankwah‐Amoah ◽  
Rexon T. Nting ◽  
Godfred Adjapong Afrifa
Keyword(s):  
Information Technology ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
And Gender

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

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