AIMC Topic: Africa South of the Sahara

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

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
This 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 ...
Humans Machine Learning Female Infant, Newborn Male Infant Surveys and Questionnaires Africa South of the Sahara Infant Mortality Mothers
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Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.

Tropical animal health and production Nov 15, 2019
This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. T...
Humans Machine Learning Female Animals Animal Husbandry Farms Surveys and Questionnaires Cattle Models, Theoretical Cluster Analysis Decision Making Africa South of the Sahara Dairying Farmers
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Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries.

BMC public health May 29, 2025
INTRODUCTION: Informed contraceptive choice is declared when a woman selects a methods of contraceptive after receiving comprehensive information on available alternatives, side effects, and management if adverse effect happens. Access to contracepti...
Humans Machine Learning Algorithms Female Young Adult Adult Adolescent Africa South of the Sahara Contraception Choice Behavior Birth Rate Contraception Behavior
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.

BMC medical informatics and decision making May 26, 2025
INTRODUCTION: Cervical cancer, which includes squamous cell carcinoma and adenocarcinoma, is a leading cause of cancer-related deaths globally, particularly in low- and middle-income countries (LMICs). It is preventable through early screening, but i...
Humans Machine Learning Female Adult Uterine Cervical Neoplasms Middle Aged Early Detection of Cancer Africa South of the Sahara Patient Acceptance of Health Care
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