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Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning

Zahid Ullah1, Farrukh Saleem1, Mona Jamjoom2

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

Journal of Medical Internet Research
|June 4, 2021
PubMed
Summary

This study developed artificial intelligence models to predict childbirth mode, improving decision-making for maternity care. Data enrichment enhanced prediction accuracy, aiding practitioners in critical cases.

Keywords:
cesareandecision makingdeliveryhealth caremachine learningprediction model

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning Applications
  • Maternity Care Decision Support

Background:

  • Artificial intelligence (AI) is transforming healthcare by enabling data-driven insights for effective decision-making.
  • Maternity care generates vast amounts of data, posing challenges for timely and accurate clinical decisions.
  • Predicting childbirth mode (vaginal vs. cesarean delivery) is crucial for optimizing maternal and fetal outcomes.

Purpose of the Study:

  • To develop reliable prediction models for a maternity care decision support system.
  • To accurately forecast the mode of delivery prior to childbirth using machine learning.
  • To enhance clinical decision-making in obstetrics through predictive analytics.

Main Methods:

  • The study involved data enrichment and analysis of historical medical records.
  • Machine learning algorithms including decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor were employed.
  • Models were trained and evaluated on both original and enriched datasets to identify the mode of childbirth.

Main Results:

  • Prediction models utilizing enriched datasets demonstrated superior performance across accuracy, sensitivity, specificity, and F-measure.
  • The k-nearest neighbor model achieved the highest accuracy at 84.38%, followed by bagging (83.75%) and random forest (83.13%).
  • Data enrichment significantly improved the accuracy of delivery mode prediction, offering valuable support to maternity care practitioners.

Conclusions:

  • Enriching datasets is crucial for enhancing the accuracy of predictive models in maternity care.
  • The developed models support healthcare providers in making informed decisions, particularly in critical childbirth scenarios.
  • Further improvements can be achieved by incorporating larger volumes of real-world clinical data into the datasets.