Employing machine learning models to predict pregnancy termination among adolescent and young women aged 15–24 years in East Africa

Pregnancy termination is still a sensitive and continuing public health issue due to several political, economic, religious, and social concerns. This study assesses the applications of machine learning models in the prediction of pregnancy termination using data from eleven national datasets in East Africa. Nine machine learning models, namely: Random Forests (RF), Decision Tree, Logistic Regression, Support Vector Machine, eXtreme Gradient Boosting (XGB), AdaBoost, CatBoost, K-nearest neighbor, and feedforward neural network models were used to predict pregnancy termination, with six evaluation criteria utilized to compare their performance. The pooled prevalence of pregnancy termination in East Africa was found to be 4.56%. All machine learning models had an accuracy of at least 71.8% on average. The RF model provided accuracy, specificity, precision, and AUC of 92.9%, 0.87, 0.91, and 0.93, respectively. The most important variables for predicting pregnancy termination were marital status, age, parity, country of residence, age at first sexual activity, exposure to mass media, and educational attainment. These findings underscore the need for a tailored approach that considers socioeconomic and regional disparities in designing policy initiatives aimed at reducing the rate of pregnancy terminations among younger women in the region.