BMC pregnancy and childbirth · 2025
Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa.
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Yehuala Tirualem Zeleke, Fente Bezawit Melak, Wubante Sisay Maru
Аннотация
BACKGROUND: The occurrence of short birth intervals among reproductive-age women in East Africa is a critical public health issue, contributing to maternal and child health risks. Identifying the key factors that predict short birth intervals can help design targeted interventions to reduce these risks. Hence, this study aimed to predict short birth intervals and identify their determinants using supervised machine learning models.
METHOD: This study employs machine learning algorithms to predict short birth intervals among reproductive-age women in East Africa, using a dataset from Demographic and Health Surveys. The dataset undergoes preprocessing steps to handle missing values, encode categorical variables, perform feature selection, and integrate data and normalize numerical features. Four machine learning models, including logistic regression, decision trees, random forests, and some machine learning models, including logistic regression, decision trees, random forests, and naive Bayes, are trained and evaluated to predict short birth intervals. Model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC used to ensure reliable results.
RESULT: The machine learning models identified several key factors that significantly predict short birth intervals among reproductive-age women in East Africa. The Random Forest models demonstrated the highest accuracy (79.4%), precision (79.0%), F-score (84.0%), ROC curve (83.8%), and recall (91.0%), with feature importance analysis highlighting maternal age, educational status, parity, use of family planning, and access to healthcare as the most influential predictors. The findings underscore the importance of targeted interventions addressing healthcare access and family planning to reduce the risks associated with short birth intervals in East African countries.
CONCLUSION: The study demonstrates that machine learning models can effectively identify key predictors of short birth intervals among reproductive-age women in East Africa, providing valuable insights for designing targeted public health interventions to improve maternal and child health outcomes in East Africa.
METHOD: This study employs machine learning algorithms to predict short birth intervals among reproductive-age women in East Africa, using a dataset from Demographic and Health Surveys. The dataset undergoes preprocessing steps to handle missing values, encode categorical variables, perform feature selection, and integrate data and normalize numerical features. Four machine learning models, including logistic regression, decision trees, random forests, and some machine learning models, including logistic regression, decision trees, random forests, and naive Bayes, are trained and evaluated to predict short birth intervals. Model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC used to ensure reliable results.
RESULT: The machine learning models identified several key factors that significantly predict short birth intervals among reproductive-age women in East Africa. The Random Forest models demonstrated the highest accuracy (79.4%), precision (79.0%), F-score (84.0%), ROC curve (83.8%), and recall (91.0%), with feature importance analysis highlighting maternal age, educational status, parity, use of family planning, and access to healthcare as the most influential predictors. The findings underscore the importance of targeted interventions addressing healthcare access and family planning to reduce the risks associated with short birth intervals in East African countries.
CONCLUSION: The study demonstrates that machine learning models can effectively identify key predictors of short birth intervals among reproductive-age women in East Africa, providing valuable insights for designing targeted public health interventions to improve maternal and child health outcomes in East Africa.