ENSEMBLE PREDICTIVE MODELS FOR CONTRACEPTIVE UPTAKE IN WOMEN

Abstract: Contraceptive uptake among women plays a pivotal role in reproductive health outcomes and family planning initiatives worldwide. In recent years, the application of predictive modeling techniques has gained significant attention in understanding and forecasting contraceptive usage patterns. This paper presents a comprehensive review of the latest advancements in the development of ensemble predictive models for contraceptive uptake in women. The review begins with an overview of the global landscape of contraceptive utilization, highlighting the importance of accurate predictive models in addressing various challenges, including unintended pregnancies, maternal mortality, and disparities in access to reproductive healthcare services. Subsequently, the paper delves into the methodological aspects of ensemble modelling, elucidating the principles underlying ensemble learning and its relevance in enhancing predictive accuracy and robustness. Furthermore, the review synthesizes recent studies and methodologies employed in the development of ensemble predictive models for contraceptive uptake. It explores diverse data sources utilized in model training, ranging from demographic and socio-economic indicators to behavioural factors and healthcare utilization patterns. Moreover, the review discusses the integration of advanced statistical techniques, machine learning algorithms, and data fusion methodologies to capture the multifaceted determinants influencing contraceptive decision-making among women. The paper also examines the performance evaluation metrics employed to assess the predictive capabilities of ensemble models, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Moreover, it discusses the implications of ensemble predictive modeling in informing policy-making, resource allocation, and targeted interventions aimed at promoting contraceptive uptake and improving reproductive health outcomes. Additionally, the review highlights the challenges and limitations associated with ensemble predictive modeling in the context of contraceptive uptake, including data quality issues, model interpretability, and ethical considerations related to privacy and confidentiality. It underscores the need for ongoing research efforts to address these challenges and refine predictive models to better serve the needs of diverse populations. Keywords: Contraceptive, family planning, machine learning, AUC-ROC. Title: ENSEMBLE PREDICTIVE MODELS FOR CONTRACEPTIVE UPTAKE IN WOMEN Author: Joda Shade Christiana, Prof. O.O. Obe, Prof. O.K. Boyinbode, Ipaye abiodun International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 12, Issue 4, October 2024 - December 2024 Page No: 56-63 Research Publish Journals Website: www.researchpublish.com Published Date: 12-December-2024 DOI: https://doi.org/10.5281/zenodo.14411559 Paper Download Link (Source) https://www.researchpublish.com/papers/ensemble-predictive-models-for-contraceptive-uptake-in-women