2023
DOI: 10.1371/journal.pdig.0000345
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Machine learning modeling for identifying predictors of unmet need for family planning among married/in-union women in Ethiopia: Evidence from performance monitoring and accountability (PMA) survey 2019 dataset

Shimels Derso Kebede,
Daniel Niguse Mamo,
Jibril Bashir Adem
et al.

Abstract: Unmet need for contraceptives is a public health issue globally that affects maternal and child health. Reducing unmet need reduces the risk of abortion or childbearing by preventing unintended pregnancy. The unmet need for family planning is a frequently used indicator for monitoring family planning programs. This study aimed to identify predictors of unmet need for family planning using advanced machine learning modeling on recent PMA 2019 survey data. The study was conducted using secondary data from PMA Et… Show more

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Cited by 3 publications
(5 citation statements)
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“…In this study supervised machine learning algorithms such as Random Forest, Ada Boost, Gaussian NB, MLP, Decision Tree, Logistic Regression (LR), random forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XG Boost), and support vector machines (SVM) (25)(26)(27)(28), was performed to predict determinants of home delivery after ANC visit among reproductive age women in East Africa. A tenfold cross-validation method was used for training the models on training data.…”
Section: Data Management and Analysismentioning
confidence: 99%
See 4 more Smart Citations
“…In this study supervised machine learning algorithms such as Random Forest, Ada Boost, Gaussian NB, MLP, Decision Tree, Logistic Regression (LR), random forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XG Boost), and support vector machines (SVM) (25)(26)(27)(28), was performed to predict determinants of home delivery after ANC visit among reproductive age women in East Africa. A tenfold cross-validation method was used for training the models on training data.…”
Section: Data Management and Analysismentioning
confidence: 99%
“…The relationship between the predictors and the outcome variable was evaluated using the SHAP feature importance method, which also helped identify the independent variables that are most crucial for predicting home delivery after an ANC visit. The Shapley Additive exPlanations (SHAP) analysis employs a game theory framework to provide a global or local interpretation and explanation of any machine learning model's prediction (27). Since tree-based models are typically "black-box" systems, it is uncommon to nd interpretations and explanations of high-performing models in machine learning research (27).…”
Section: Data Management and Analysismentioning
confidence: 99%
See 3 more Smart Citations