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Meta-Explainable Machine Learning Model for Public Health Care Resource Management

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Publicado en:Journal of Database Management vol. 36, no. 1 (2025), p. 1-29
Autor principal: Patel, Shivshanker Singh
Publicado:
IGI Global
Materias:
Womens health
Public health
Health care policy
Maternal mortality
Health services
Machine learning
Unsupervised learning
Regions
Resource allocation
Health status
Resource management
Health initiatives
Global health
COVID-19
Clinical outcomes
Artificial intelligence
Maternal & child health
Health care
Maternal characteristics
Decision making
Optimization
Mothers
Acceso en línea:Citation/Abstract
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Resumen:This study introduces a new meta-explainable machine learning methodology to enhance medical care recommendations and optimize healthcare operations through targeted interventions. It could assist a large, and diverse population facing challenges in resource allocation and operational complexity. The proposed method utilizes a two-stage model. It first employs an Explainable Boosting Machine (EBM) and then provides the output from the initial phase to an unsupervised machine learning framework. It examines diverse aspects to identify the most critical set of features for focused operations and policy recommendations in designated areas. The research is based on data collected from three regions of India about maternal health and maternal mortality. The results highlight the accuracy of healthcare operations, thereby facilitating data-informed decisions. Implementing the method outlined in this paper in any other region across the globe will significantly enhance the design and execution of targeted healthcare initiatives, enhancing public health outcomes and optimizing resource.
ISSN:1063-8016
1533-8010
1047-9430
DOI:10.4018/JDM.386382
Fuente:ABI/INFORM Global