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 Videos

Predicting clinical outcomes for newborns using two artificial intelligence approaches.

M Frize1, D Ibrahim, H Seker

  • 1MIRG, Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
Summary

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

Assessment of leukocyte telomere length as a cellular aging marker through a quantitative PCR-based technique in individuals with a chronic addiction to the psychoactive drug delta-9-tetrahydrocannabinol. (Case control study).

La Clinica terapeutica·2025
Same author

Virus neutralization test for confirmation of ELISA-positive SARS-CoV-2 antibodies in dogs.

Polish journal of veterinary sciences·2024
Same author

A national survey of the provision of prehabilitation for oesophagogastric cancer patients in the UK.

Annals of the Royal College of Surgeons of England·2024
Same author

Exercise During Chemotherapy for Cancer: A Systematic Review.

Journal of surgical oncology·2024
Same author

Effect of cereal type and plant extract addition on the growth performance, intestinal morphology, caecal microflora, and gut barriers gene expression of broiler chickens.

Animal : an international journal of animal bioscience·2021
Same author

Adiponectin resistance parameter as a marker for high normal blood pres-sure and hypertension in patients with metabolic syndrome.

Hippokratia·2020

Artificial neural networks (ANN) and fuzzy logic (FLC) were compared for predicting newborn outcomes. Both methods showed comparable results, with ANN slightly outperforming FLC in sensitivity for this large dataset.

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Neonatal Care

Background:

  • Accurate prediction of neonatal outcomes is crucial for timely medical intervention.
  • Artificial intelligence (AI) methods offer potential for improving predictive accuracy in healthcare.
  • Comparing different AI approaches is essential to determine optimal methods for specific clinical applications.

Purpose of the Study:

  • To compare the predictive performance of artificial neural networks (ANN) and fuzzy logic classifiers (FLC) for neonatal outcomes.
  • To evaluate whether ANN and FLC perform comparably or differently across various neonatal outcome predictions.
  • To assess the potential strengths and weaknesses of ANN and FLC in neonatal outcome prediction.

Main Methods:

  • Utilized a large, single neonatal database for analysis.

Related Experiment Videos

  • Applied artificial neural networks (ANN), specifically back-propagation feed-forward networks.
  • Employed fuzzy logic classifiers (FLC) for comparative analysis.

    • Main Results:

    • Correct Classification Rate (CCR) and Specificity were comparable between ANN and FLC.
    • Sensitivity (true positive rate) was slightly higher for the ANN compared to the FLC.
    • The study utilized a very large database, which may influence the observed performance differences.

    Conclusions:

    • Both ANN and FLC are viable methods for predicting neonatal outcomes.
    • ANN demonstrated a slight advantage in sensitivity over FLC in this large dataset.
    • Future research should explore FLC performance on smaller datasets and consider hybrid approaches for neonatal prediction.