ItemBook Chapter

Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm

Title
Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm
Creator
Natarajan, Jayapandian; Vimal, Parekh Hetvi; Sen, Dikshita; Lawrence, Cyril; Eswaran, Sivaraman; Chintale, Pradeep; Patil, Shalmali Arun
Description
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.
Source
Revolutionizing Healthcare Services: Unleashing Innovation through Generative AI;pp.251-266
Date
01-01-2025
Publisher
CRC Press
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104035711-8; 978-103287158-5;
Format
online
Language
English
Type
Book chapter

Citation

Natarajan, Jayapandian; Vimal, Parekh Hetvi; Sen, Dikshita; Lawrence, Cyril; Eswaran, Sivaraman; Chintale, Pradeep; Patil, Shalmali Arun, “Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24366.

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