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

Shivshanker Singh Patel

Journal of Database Management2025https://doi.org/10.4018/jdm.386382article
AJG 1ABDC A
Weight
0.50

Abstract

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.

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https://doi.org/https://doi.org/10.4018/jdm.386382

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@article{shivshanker2025,
  title        = {{Meta-Explainable Machine Learning Model for Public Health Care Resource Management}},
  author       = {Shivshanker Singh Patel},
  journal      = {Journal of Database Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/jdm.386382},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.