habtamusetegn/Predicting-and-Identifying-Determinant-Factors-for-Adverse-Birth-Outcomes

Title: Predicting and Identifying Determinant Factors for Adverse Birth Outcomes in Sub-Saharan Africa: A Data-Driven Investigation with Advanced Machine Learning Techniques # Predicting-and-Identifying-Determinant-Factors-for-Adverse-Birth-Outcomes Title: Predicting and Identifying Determinant Factors for Adverse Birth Outcomes in Sub-Saharan Africa: A Data-Driven Investigation with Advanced Machine Learning Techniques This study utilized secondary data from the most recent Demographic and Health Surveys (DHS) conducted in the following 26 Sub-Saharan Africa countries: Ethiopia (2016), Kenya (2022), Uganda (2016), Tanzania (2022), Burundi (2017), Rwanda (2015), Madagascar (2021), Mozambique (2015), Zimbabwe (2015), Zambia (2018), Malawi (2016), and so on (Refer the whole document after publication). The study specifically targeted women of reproductive age in East Africa, those between the ages of 15 and 49 who had given birth within the past five years. The analysis included a substantial sample size of 139,659 individuals across the 26 countries mentioned. The dataset used in the study consisted of 29 distinct features that were taken into account during the analysis. In this study, the primary measure utilized was adverse birth outcomes, which were determined based on the availability of relevant items in the DHS (Demographic and Health Survey). These outcomes encompassed the occurrence of one or more specific conditions during recent pregnancies, such as low birth weight, macrosomia, preterm birth, or stillbirth. The researchers combined these factors, including low birth weight, macrosomia, stillbirth, and gestational age of less than 37 weeks, to create a single variable. This variable was assigned a value of 1 if any adverse birth outcomes were reported, indicating the presence of an adverse birth outcome, and a value of 0 if none of these outcomes were reported. After conducting a literature review, the study examined numerous independent variables. These variables encompassed various factors, such as place of residence, maternal education, gender of the household head, ownership of a mobile phone, mode of delivery (cesarea …