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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Skin lesion segmentation using deep learning algorithm with ant colony optimization.

BMC medical informatics and decision making·2024
Same author

Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking.

Biomimetics (Basel, Switzerland)·2023
Same author

Gaussian-Filtered High-Frequency-Feature Trained Optimized BiLSTM Network for Spoofed-Speech Classification.

Sensors (Basel, Switzerland)·2023
Same author

Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder.

Biology·2022
Same author

A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning.

Computational and mathematical methods in medicine·2022
Same author

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

Computational intelligence and neuroscience·2022

Related Experiment Video

Updated: Sep 25, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.0K

Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification.

Md Takbir Alam1, Md Ashibul Islam Khan1, Nahian Nakiba Dola1

  • 1Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh.

Applied Bionics and Biomechanics
|May 2, 2022
PubMed
Summary

A new machine learning model using cardiotocography (CTG) data accurately classifies fetal health. The random forest model achieved 97.51% accuracy, improving upon traditional methods for assessing fetal well-being during pregnancy.

More Related Videos

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

6.8K
Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System
07:34

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

Published on: May 5, 2018

11.8K

Related Experiment Videos

Last Updated: Sep 25, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.0K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

6.8K
Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System
07:34

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

Published on: May 5, 2018

11.8K

Area of Science:

  • Medical technology
  • Artificial intelligence in healthcare
  • Fetal monitoring

Background:

  • Cardiotocography (CTG) is crucial for assessing fetal health during pregnancy by monitoring fetal heart rate and uterine contractions.
  • Traditional manual analysis of CTG data is time-consuming and prone to unreliability.
  • Developing automated fetal health classification models can enhance diagnostic efficiency and conserve medical resources.

Purpose of the Study:

  • To develop and evaluate machine learning models for accurate fetal health classification using cardiotocography data.
  • To compare the performance of various machine learning algorithms for this classification task.
  • To identify the most effective machine learning model for improving the diagnosis of fetal conditions.

Main Methods:

  • Utilized an open-access cardiotocography dataset for model development and analysis.
  • Applied several machine learning algorithms including Random Forest (RF), Logistic Regression, Decision Tree (DT), Support Vector Classifier, Voting Classifier, and K-Nearest Neighbor.
  • Compared the classification performance across the implemented machine learning models.

Main Results:

  • The Random Forest (RF) model demonstrated superior performance compared to other evaluated algorithms.
  • The RF model achieved a high accuracy of 97.51% in fetal health classification.
  • This accuracy surpasses previously reported methods for CTG data analysis.

Conclusions:

  • Machine learning, particularly the Random Forest model, offers a highly accurate and efficient approach to fetal health classification.
  • Automated analysis of CTG data using ML can significantly improve diagnostic accuracy and efficiency in obstetrics.
  • The developed ML model shows promise for clinical application in prenatal care, aiding in the identification of potentially pathologic fetuses.