Machine Learning and Multiple Abortions - Pradeep Kumar; Catia Nicodemo; Sonia Oreffice; Climent Quintana-Domeque (Institute of Labor Economics (IZA), 2024)
This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and assess monetary savings through targeted interventions. We find that Random Forest and an Ensemble method are most effective in the highest risk decile, capturing about 55% of cases, whereas linear models and Extreme Gradient Boosting excel in mid to lower deciles. We also show that targeting the top 20% most at-risk could yield cost savings of 5.44 to 8.2 million EUR, which could be reallocated to prevent unintended pregnancies arising from contraceptive failure, abusive relationships, and sexual assault, among other factors.
Keywords: I12; I18; C53; J13; C55; 330; Extreme Gradient Boosting; Ridge; random forest; multiple abortions; Logit; Lasso; Ensemble; reproductive healthcare