Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa

The Random Forest machine learning model demonstrates greater predictive power in estimating pregnancy loss risk factors. Machine learning can help facilitate early prediction and intervention for women at high risk of pregnancy loss. Based on these findings, we recommend policy measures aimed at reducing pregnancy loss Sub-Saharan African countries.