Machine Learning and Multiple Abortions

Pradeep Kumar, Catia Nicodemo (), Sonia Oreffice and Climent Quintana-Domeque
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Pradeep Kumar: University of Exeter
Catia Nicodemo: University of Oxford

No 17046, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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: Extreme Gradient Boosting; Ridge; random forest; multiple abortions; Logit; Lasso; Ensemble; reproductive healthcare (search for similar items in EconPapers)
JEL-codes: C53 C55 I12 I18 J13 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2024-06
New Economics Papers: this item is included in nep-big and nep-cmp
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