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Ensemble machine learning framework for predicting maternal health risk during pregnancy.

Alaa O Khadidos1,2, Farrukh Saleem3, Shitharth Selvarajan4,5

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Scientific Reports
|September 14, 2024
PubMed
Summary

This study introduces a machine learning framework to predict maternal health risks, identifying high blood pressure and sugar as key factors. Early detection of high-risk pregnancies can improve outcomes and save lives.

Keywords:
Ensemble machine learningMachine learningMaternal health riskPregnancy complications

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Public Health

Background:

  • Maternal health risks, including hypertension and glucose issues, contribute to pregnancy complications.
  • Early identification and monitoring of these risks are crucial for reducing adverse outcomes.
  • Existing methods may lack the precision needed for timely and effective intervention.

Purpose of the Study:

  • To develop and implement a machine learning framework for predicting Maternal Health Risk (MHR) factors.
  • To identify and classify maternal health risks using real-world datasets.
  • To improve the accuracy of predicting high-risk pregnancies.

Main Methods:

  • Developed the Quad-Ensemble Machine Learning framework (QEML-MHRC).
  • Integrated multiple Machine Learning (ML) models with four ensemble ML techniques.
  • Conducted nineteen training and testing experiments on data from maternity hospitals and clinics.

Main Results:

  • Exploratory data analysis identified high blood pressure, low blood pressure, and high blood sugar as significant risk factors.
  • The QEML-MHRC framework demonstrated outstanding predictive performance across all risk classes.
  • The "HR" (High Risk) class achieved 90% prediction accuracy; Gradient Boosting Trees (GBT) with ensemble stacking showed 0.86 performance across all classes.

Conclusions:

  • The proposed QEML-MHRC approach effectively assesses maternal health risks, enabling early detection of high-risk pregnancies.
  • Class-wise performance measurement enhances the understanding of risk distinctions (high, low, medium).
  • This predictive model can aid medical experts in timely interventions, potentially saving lives and reducing complications.