Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries.
Journal:
BMC public health
Published Date:
May 29, 2025
Abstract
INTRODUCTION: Informed contraceptive choice is declared when a woman selects a methods of contraceptive after receiving comprehensive information on available alternatives, side effects, and management if adverse effect happens. Access to contraceptive information is a fundamental right, crucial for reducing fertility and unintended pregnancies and related complications. Despite efforts to reduce fertility, Sub-Saharan Africa region is still accounts for over half of the global births due to low contraceptive use, high discontinuation rate, and unmet needs, often linked to uninformed contraceptive choice. While studies on informed contraceptive choice are available using classical regression analysis, the diverse nature of factors have not been systematically analyzed using machine learning algorithms. Hence, this study aimed to apply machine learning algorithms to model predictors of informed contraceptive choices among reproductive age women in six high fertility rate Sub Sahara Africa countries.