Strategies to reduce fetal mortality in adolescent pregnancies

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Monica Gabriela Chachalo Sandoval
Yesenia Mishell Revelo Requena
Scarleth Samantha Congo Méndez
Monica Priscila Pazmiño Imbaquingo

Abstract

The study aimed to evaluate and prioritize strategies to reduce fetal mortality in adolescent pregnancies in Ecuador, using an innovative approach that combined multi-criteria decision-making methods and neutrosophic logic. Secondary data from official sources corresponding to the period 2019-2022 were analyzed. Neutrosophic logic allowed for the management of uncertainty and vagueness present in the data, while the TOPSIS method facilitated the prioritization of strategies based on evaluation criteria. The results showed that strengthening comprehensive sexual education programs was the most effective strategy, with a higher proximity coefficient, highlighting its adaptability to diverse sociocultural contexts. Other interventions showed potential but faced limitations in their technical feasibility and sustainability. The application of neutrosophic logic enabled the integration of different variables, providing a more comprehensive and realistic evaluation of the strategies. The study demonstrated the usefulness of multi-criteria methods and neutrosophic logic in addressing complex public health problems, where information is incomplete or contradictory. These approaches not only allowed for the systematic prioritization of interventions but also identified areas for improvement and opportunities for future research. The findings provided a robust methodological framework for decision-making in dynamic contexts, contributing to the design of more effective and adaptive public policies tailored to the needs of vulnerable populations.

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How to Cite
Strategies to reduce fetal mortality in adolescent pregnancies. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 726-735. https://fs.unm.edu/NCML2/index.php/112/article/view/765
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How to Cite

Strategies to reduce fetal mortality in adolescent pregnancies. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 726-735. https://fs.unm.edu/NCML2/index.php/112/article/view/765

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