Page 15 - April_2024 Broucher.indd

Shivshanker Singh Patel

Title: Explainable machine learning models to analyse maternal health

Journal: Data and Knowledge Engineering

Maternal health is a significant public health concern for globe and

many developing countries. A country like India (with large population),
there are considerable disparities in maternal health service utilisation
and maternal mortality within and across states. A more than a general
healthcare operational policy would suffice, but a precision healthcare
strategy would be needed. This article focused on explainable machine

learning models that can precisely advise health care intervention policy
and medical treatment to an administrative unit rather than a generic
policy suggestion for improving maternal health. This study presents an

exhaustive list of factors associated with Maternal Mortality Rate (MMR) and a series of explainable AI models. One of
models uses CART heuristics to categorise districts (administrative boundaries) into lower and higher MMR classes.
Another explainable model, Shapley Additive Explanations (SHAP), used SVM, ANN, boosting, and random forest

machine learning models to investigate higher and lower MMR
regions. Further, an Explainable Boosting Machine (EBM) also
used, and the results are compared for policy suggestions. Some
of the ignored features from general social science studies,

such as topography and agro-climatic zone characteristics of
a particular district, may be crucial in the analysis. Moreover,
health infrastructure, insurance, and other factors also influence
policymaking. This predictive and explainable work has

significant implications for precision healthcare policy design
to improve maternal health compared to a broader policy
approach.

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