Artificial Intelligence-Driven Perspectives on Maternal Health: Revealing Important Aspects and Improving Pregnancy Results via Machine Learning
Healthcare interventions; Machine learning; Maternal health; Predictive modelling; Pregnancy outcomes
A number of factors, including genetic, environmental and social ones, affect the intricate biological process of pregnancy. The developing foetuss health as well as the mothers must be maintained in the necessary secure equilibrium of these variables. The mothers health, which encompasses her mental as well as physical health, lifestyle decisions, money, social support systems and educational attainment, will determine whether the pregnancy ends well. Medical research has changed as a result of the long-awaited tools for processing for complicated datasets that have been made possible by recent advancements in machine learning models. These models have the ability to identify correlations between characteristics that are difficult for traditional analytical techniques to uncover. Therefore, scientists can improve their understanding of the elements influencing conception and create diagnostic tools by utilizing machine learning technology for timely intervention and customized treatment. Machine learning encompasses various techniques, such as logistic regression, linear regression, random forest, K-Nearest Neighbours and gradient boosting classifier. While Random Forest is an effective way to handle big databases with multiple dimensions and interactions, KNN classifiers are excellent for more organic, data-driven cluster finding of relevant instances and association investigation between various parameters and pregnancy outcomes. Logistic regression only explains the ways in which individual factors affect pregnancy outcomes; it cannot handle binary outcomes as well as linear regression does. We will look for significant determinants of pregnancy outcomes and assess each models performance. Important elements will also be expanded upon. Pregnant patients care, professional practice and improved program decisions may all benefit from this information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.227-243
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-4880-1_18 https://www.scopus.com/pages/publications/105019292392?origin=resultslist
Shradha V.S., Computer Science and Engineering, Christ University, Bangalore, India; Prathap B.R., Department of Computer Science and Engineering, M S Ramaiah University of Applied Sciences, Bangalore, India