Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 3, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.

Chalie Mulugeta1, Tadele Emagneneh2, Aynalem Yetwale2

  • 1Department of Midwifery, College of Health Science, Woldia University, Woldia City, Ethiopia. chaliemulu19@gmail.com.

Scientific Reports
|June 1, 2026
PubMed
Summary

Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Determinants and predictive modeling of long-acting reversible contraceptive use in Sub-Saharan Africa: evidence from DHS data using machine learning and association rule mining.

AJOG global reports·2026
Same author

Determinants of Recovery From Obstetric Fistula in Ethiopia: A Systematic Review and Meta-Analysis.

Nursing open·2026
Same author

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

Acta obstetricia et gynecologica Scandinavica·2026
Same author

Global, regional and national burden of maternal haemorrhage (2000-2021) and projections to 2050 in 204 countries and territories.

BMJ open·2026
Same author

Complementary feeding practices and associated factors among mothers of children aged 6-23 months in Debre Berhan town, Ethiopia.

Scientific reports·2026
Same author

Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review.

Diagnostic and prognostic research·2026

Machine learning models can predict postpartum convulsions, a major cause of maternal death in Ethiopia. Neural networks showed the best performance, identifying key risk factors like migraines and abdominal pain during pregnancy.

Area of Science:

  • Maternal Health
  • Machine Learning in Healthcare
  • Public Health Informatics

Background:

  • Postpartum convulsions are a significant contributor to maternal morbidity and mortality in Ethiopia.
  • Predicting and preventing these events is crucial for improving maternal outcomes.

Purpose of the Study:

  • To develop and validate machine learning models for predicting postpartum convulsions.
  • To identify key clinical indicators for early risk detection using routinely collected data.

Main Methods:

  • Eight machine learning algorithms were developed and evaluated using the Performance Monitoring for Action (PMA) Ethiopia dataset.
  • Feature selection was performed using Recursive Feature Elimination (RFE), and class imbalance was managed with up-sampling techniques.
  • Model performance was assessed using accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC), with interpretability via Shapley Additive Explanations (SHAP).
Keywords:
EclampsiaMachine learningPostpartum convulsionPreeclampsiaRisks

Related Experiment Videos

Last Updated: Jun 3, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Main Results:

  • The Neural Network model demonstrated the highest predictive performance with an AUC of 0.868, followed by Support Vector Machine (0.833) and Random Forest (0.822).
  • Key predictors identified by SHAP analysis included migraine, convulsion, and abdominal pain during pregnancy, alongside ANC visit timing, blood pressure, and urine testing.
  • Lack of treatment for pregnancy-related complications was also a significant factor.

Conclusions:

  • Machine learning models, especially Neural Networks, show strong potential for predicting postpartum convulsions using existing clinical data.
  • Explainable AI (SHAP) can identify high-risk individuals, enabling timely interventions to reduce maternal complications in resource-limited settings.