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Predicting maternal risk level using machine learning models.

Sulaiman Salim Al Mashrafi1,2, Laleh Tafakori3, Mali Abdollahian3

  • 1School of Science, RMIT University, Melbourne, Victoria, Australia. S3912607@student.rmit.edu.au.

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|December 18, 2024
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Summary
This summary is machine-generated.

Machine learning models can predict maternal health risks. The Random Forest model showed the best performance in identifying high-risk pregnancies, aiding early intervention to reduce maternal mortality.

Keywords:
Machine learningMaternal mortality ratioMaternal mortality riskOmanPredictionPrincipal Component Analysis (PCA)

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Area of Science:

  • Public Health
  • Medical Informatics
  • Machine Learning

Background:

  • Maternal morbidity and mortality are critical global health issues, with reducing the maternal mortality ratio (MMR) being a key Sustainable Development Goal (SDG).
  • Accurate prediction of maternal health risks is essential for targeted interventions but remains challenging.
  • Machine learning (ML) offers a promising approach to developing accurate predictive models for maternal health outcomes.

Purpose of the Study:

  • To explore the efficacy of various ML algorithms for predicting maternal risk levels.
  • To utilize a nationwide Omani maternal mortality dataset for ML-based risk prediction.
  • To establish a foundation for data-driven strategies to mitigate maternal mortality.

Main Methods:

  • Utilized a dataset of 402 maternal deaths in Oman (1991-2023).
  • Applied and compared ten ML algorithms, including Random Forest (RF), with and without Principal Component Analysis (PCA).
  • Assessed model performance using metrics such as accuracy, sensitivity, precision, and F1-score.

Main Results:

  • The Random Forest (RF) model, after applying PCA, demonstrated superior performance in predicting maternal risk levels.
  • The optimized RF model achieved an accuracy of 75.2%, precision of 85.7%, and an F1-score of 73% in classifying risk.
  • This indicates the potential of ML, specifically RF, in accurately identifying high-risk maternal cases.

Conclusions:

  • Machine learning models were successfully applied to predict maternal risk levels using Omani data.
  • The Random Forest algorithm proved most effective for this classification task.
  • Accurate maternal risk prediction can significantly aid healthcare providers in developing timely intervention plans to decrease maternal mortality.