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 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predicting breast screening attendance using machine learning techniques.

Vikraman Baskaran1, Aziz Guergachi, Rajeev K Bali

  • 1Ryerson University, TRSM, Toronto, Canada. vikraman@ryerson.ca

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|January 11, 2011
PubMed
Summary
This summary is machine-generated.

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

Data-Driven and Expert-Informed Causal Discovery for Type 2 Diabetes Risk in Primary Care.

Studies in health technology and informatics·2026
Same author

Identifying Clinical Predictors of Diabetes and Prediabetes: An Explainable AI Approach Using Primary Care Electronic Medical Records.

Studies in health technology and informatics·2026
Same author

Workplace inequities and health outcomes among Black professionals in Canada.

PloS one·2025
Same author

Identifying Prediabetes in Canadian Populations Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Predicting Diabetes in Canadian Adults Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Predicting Time to Diabetes Diagnosis Using Random Survival Forests.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Categorization and segmentation of intestinal content frames for wireless capsule endoscopy.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013
Same journal

An intelligent scoring system and its application to cardiac arrest prediction.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013
Same journal

Guest editorial: Multimedia services and technologies for e-health (MUST-EH).

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013
Same journal

Editorial: From “information technology in biomedicine” to “biomedical and health informatics”.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013
Same journal

Equipment location in hospitals using RFID-based positioning system.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013
Same journal

Distributed system for cognitive stimulation over interactive TV.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2012
See all related articles

This study introduces a novel machine learning algorithm to predict breast screening attendance, achieving nearly 80% accuracy. Further research is needed to improve negative predictive value and specificity for this important healthcare application.

Area of Science:

  • Healthcare Informatics
  • Machine Learning Applications
  • Biomedical Data Science

Background:

  • Machine learning is increasingly used in healthcare prediction.
  • Predicting breast screening attendance before mammography is an emerging research area.
  • Accurate prediction can optimize screening program resource allocation and patient outreach.

Purpose of the Study:

  • To introduce novel predictor attributes for breast screening attendance prediction.
  • To present a new hybrid machine learning algorithm combining back-propagation and radial basis function neural networks.
  • To evaluate the algorithm's accuracy and efficiency using a large, long-term dataset.

Main Methods:

  • Development of a hybrid neural network algorithm integrating back-propagation and radial basis function networks.

Related Experiment Videos

Last Updated: Jun 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

  • Utilization of a comprehensive 13-year dataset (1995-2008) for algorithm training and testing.
  • Validation of algorithm performance across different computational platforms.

    • Main Results:

    • The developed algorithm achieved approximately 80% accuracy.
    • High positive predictive value (88%) and sensitivity (88%) were recorded.
    • Negative predictive value and specificity were in the range of 40-50%, indicating areas for improvement.

    Conclusions:

    • The hybrid machine learning algorithm shows promising results for predicting breast screening attendance.
    • The recorded accuracy, sensitivity, and positive predictive value support further large-scale testing.
    • Enhancements to negative predictive value and specificity are recommended for future iterations.