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<p>Pakistan is unable to meet its maternal and child health targets. Predictive machine learning has the potential to predict high risk pregnancies based on data from women who have had a miscarriage or stillbirth. This would help advise better healthcare plans at primary and tertiary level and help achieve Sustainable Development Goal targets in the country. The aim of this study was to evaluate several machine learning models to measure their ability to detect high risk pregnancies. The Pakistan Demographic Health Survey (2018) has been used which includes data from 15,068 women across Pakistan. Fourteen machine learning classifiers have been employed to predict high risk pregnancies, with the following evaluation metrics reported: precision, recall, false positive rate (FPR), accuracy, and F1-score. We find that five models have the highest overall performance: (i) Deep Neural Network, (ii) SELU Network, (iii) Multilayer Perceptron, (iv) Gradient Boosting, and (v) AdaBoost, exhibiting near good precision (73.0-76.0%), effective recall (83.0-86.0%), robust accuracy (89.0-90.0%), and decent F1-Scores (79.0-80.0%). This study recommends the integration of low-cost online models to predict high risk pregnancies as a critical tool to help achieve maternal health targets in the country.</p>

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Predicting high risk pregnancies in Pakistan- a demographic assessment using predictive machine learning

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Sara Rizvi Jafree,
  • Mian Muhammad Mubasher

摘要

Pakistan is unable to meet its maternal and child health targets. Predictive machine learning has the potential to predict high risk pregnancies based on data from women who have had a miscarriage or stillbirth. This would help advise better healthcare plans at primary and tertiary level and help achieve Sustainable Development Goal targets in the country. The aim of this study was to evaluate several machine learning models to measure their ability to detect high risk pregnancies. The Pakistan Demographic Health Survey (2018) has been used which includes data from 15,068 women across Pakistan. Fourteen machine learning classifiers have been employed to predict high risk pregnancies, with the following evaluation metrics reported: precision, recall, false positive rate (FPR), accuracy, and F1-score. We find that five models have the highest overall performance: (i) Deep Neural Network, (ii) SELU Network, (iii) Multilayer Perceptron, (iv) Gradient Boosting, and (v) AdaBoost, exhibiting near good precision (73.0-76.0%), effective recall (83.0-86.0%), robust accuracy (89.0-90.0%), and decent F1-Scores (79.0-80.0%). This study recommends the integration of low-cost online models to predict high risk pregnancies as a critical tool to help achieve maternal health targets in the country.