Machine learning-based prediction and identification of determinants of teenage pregnancy in ten East African countries

Abstract

About 21 million teenagers became pregnant annually throughout the globe. Teen pregnancy is a serious issue in Sub-Saharan Africa, with East Africa reporting the highest rates. In the field of public health, machine learning has become an invaluable tool due to its ability to process large, complex datasets and identify trends. This study uses machine learning to predict and identify key determinants of teenage pregnancy in East Africa, utilizing DHS. A supervised machine learning approach, specifically the Random Forest algorithm, was applied to analyze relationships between predictors and teenage pregnancy outcomes. Data preprocessing included handling missing values, feature scaling, and addressing class imbalance using Tomek Links and SMOTE Model performance was evaluated using metrics such as accuracy, confusion matrix, and ROC AUC. The final model was validated on a separate test set to ensure generalizability and predictive accuracy. Random Forest demonstrated superior performance, with an AUC of 94.6, an accuracy of 89.1%, an F1 score of 89%, a recall of 88%, and a precision of 90%. Kenya had the highest rate of teenage pregnancies at 19.1%, with a 95% confidence interval of [18.12%, 20.08%]. Key predictors of teenage pregnancy in East Africa include maternal education, marital status, age at first sexual intercourse, wealth status, place of residence, distance to health facilities, and social media usage. These findings suggest that expanding reproductive health services in rural areas, with strengthened youth-friendly services; promoting education about teenage pregnancy through social media; and integrating reproductive health education into school curricula may decrease teenage pregnancy in East Africa.