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Related Experiment Video

Updated: Sep 4, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage

Sujatha Krishnamoorthy¹, Yihang Liu², Kun Liu³

¹Department of Computer Science, Wenzhou-Kean University, WENZHOU KEAN UNIVERSITY, 88 UNIVE, Wenzhou, 325006, Zhejiang, China. krishnsu@kean.edu.

BMC Pregnancy and Childbirth

|July 13, 2022

Summary

This summary is machine-generated.

Postpartum hemorrhage (PPH) prediction is crucial for maternal health. This study introduces an efficient machine learning model (OBCSA-OSAE) for accurate PPH detection, improving upon traditional methods.

Keywords:

Classification Feature Selection Machine learning Metaheuristics Postpartum hemorrhage Predictive model

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Area of Science:

Obstetrics and Gynecology
Medical Informatics
Artificial Intelligence in Medicine

Background:

Postpartum hemorrhage (PPH) is a leading cause of global maternal mortality.
Current PPH assessment relies on subjective visual estimation, lacking accuracy.
Automated diagnostic tools are needed to improve PPH detection and management.

Purpose of the Study:

To develop and validate an efficient machine learning model for predicting postpartum hemorrhage (PPH).
To enhance the accuracy of PPH detection and classification using an optimized algorithm.
To introduce a novel approach for PPH prediction, addressing limitations of current methods.

Main Methods:

Implementation of an Oppositional Binary Crow Search Algorithm (OBCSA) for optimal feature selection.
Development of an Optimal Stacked Auto Encoder (OSAE) classification model.
Parameter optimization of the OSAE model using the Equilibrium Optimizer (EO).

Main Results:

The proposed OBCSA-OSAE technique demonstrated effective feature selection for PPH prediction.
The OSAE classifier, optimized with EO, achieved high accuracy in detecting PPH.
Experimental validation on a benchmark dataset confirmed the superiority of the OBCSA-OSAE approach.

Conclusions:

The OBCSA-OSAE model offers a promising automated solution for accurate PPH prediction.
This machine learning approach can significantly improve PPH diagnosis compared to traditional methods.
Further research and clinical implementation of such models could reduce maternal mortality rates.