Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Natarajan, Jayapandian; Vimal, Parekh Hetvi; Sen, Dikshita; Lawrence, Cyril; Eswaran, Sivaraman; Chintale, Pradeep; Patil, Shalmali Arun Title A name given to the resource Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Revolutionizing Healthcare Services: Unleashing Innovation through Generative AI;pp.251-266 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003531272_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003531272_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005058544?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005058544?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Natarajan J., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vimal P.H., Department of Computer Science and Engineering, Christ University, Bangalore, India; Sen D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Lawrence C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Eswaran S., Department of Electrical and Computer Engineering, Curtin University, Sarawak, Malaysia; Chintale P., SEI Investment Company, Downingtown, PA, United States; Patil S.A., Amazon AWS, Seattle, WA, United States Description An account of the resource This research uses a variety of data sources such as maternal age, health records of the mother and/or child, socioeconomic status, medical history, or prenatal care, and details of health indicators to determine the factors most decisive in increasing mortality risks. This entails data acquisition, data cleaning, data transformation and selection, and model building with an example of algorithms such as logistic regression and random forest. The trained models are checked for accuracy and their resilience level is checked using methods like SHapley Additive exPlanations and Local Interpretable Model agnostic Explanations for interpretation. The model is presented in an easy interface that doctors and health practitioners could use to make early and relevant decisions. It keeps updating the performance of established models and is a crucial way of maintaining data security for compliance with the set regulations. The rationale for this project is to offer practical recommendations for healthcare technicians so that more lives of mothers and children could be saved and maternal/child mortality decreased. Random Forest, in particular, has demonstrated superiority due to its ensemble approach, which mixes many decision trees to improve forecast accuracy and robustness. This technique can handle huge datasets with increased dimensionality and effectively lowers the overfitting risk. Additionally, Random Forest improves generalization by averaging the outputs of numerous trees, making it more tolerant to data noise and fluctuation. What makes it superior to single decision tree models is that it can handle both numerical and categorical data and handle missing values without a substantial loss of accuracy. 2025 selection and editorial matter, Babita Singla, Kumar Shalender, Nripendra Singh, and Sandhir Sharma; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104035711-8; 978-103287158-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online