Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning
| Autor: | Nosa Orobaton, Kush R. Varshney, Aisha Walcott-Bryant, Charity Wayua, William Ogallo, Komminist Weldemariam, Skyler Speakman, Victor Akinwande, Claire-Helene Mershon |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Sub saharan business.industry Neonatal mortality Infant Newborn Infant Mothers Articles Positive correlation Machine learning computer.software_genre Age and gender Machine Learning Surveys and Questionnaires Infant Mortality Medicine Health survey Humans Female Artificial intelligence Neonatal death Negative correlation business computer Africa South of the Sahara |
| Zdroj: | AMIA Annu Symp Proc |
| ISSN: | 1942-597X |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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