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: Jul 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Using T3, an improved decision tree classifier, for mining stroke-related medical data.

C Tjortjis1, M Saraee, B Theodoulidis

  • 1School of Computer Science, University of Manchester, P.O. Box 88, Manchester M60 1QD, UK. christos.tjortjis@manchester.ac.uk

Methods of Information in Medicine
|October 17, 2007
PubMed
Summary

A new data mining method, T3, significantly improves predictive accuracy for medical data, achieving 0.4% classification error in stroke prediction compared to 33.6% with existing methods.

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

Conditional-ready mouse embryonic stem cell derived macrophages enable the study of essential genes in macrophage function.

Scientific reports·2015
Same author

Case-control study of stroke and the quality of hypertension control in north west England.

BMJ (Clinical research ed.)·1997
Same author

Perihepatitis associated with pelvic infection: the Fitz-Hugh-Curtis syndrome.

The New Zealand medical journal·1982
Same author

Disturbances of pigmentation with chloroquine.

British medical journal·1959

Area of Science:

  • Medical Informatics
  • Data Mining
  • Machine Learning

Background:

  • Medical data mining offers valuable insights for clinical decision-making and research.
  • Accurate predictive models are crucial for enhancing healthcare management.
  • Existing data mining techniques have limitations in achieving optimal accuracy and clarity.

Purpose of the Study:

  • To propose T3, a novel classification method for building accurate descriptive and predictive medical models.
  • To evaluate T3's performance against established data mining techniques.
  • To identify the strengths and weaknesses of the T3 classification algorithm.

Main Methods:

  • Developed T3, a decision tree classifier allowing controlled misclassification for improved accuracy.
  • Experimented with a real-world stroke dataset to assess T3's predictive capabilities.

Related Experiment Videos

Last Updated: Jul 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

  • Compared T3's performance with a leading decision tree classifier, C4.5.

    • Main Results:

    • T3 achieved a remarkably low classification error of 0.4% on unseen stroke cases.
    • The state-of-the-art C4.5 classifier resulted in a 33.6% classification error.
    • T3 demonstrated superior predictive performance on the stroke dataset.

    Conclusions:

    • T3 is an effective classification algorithm producing highly accurate, small, and interpretable decision trees.
    • The T3 method offers strong descriptive and predictive power with enhanced simplicity.
    • Evaluation on stroke data confirmed T3's superiority over C4.5 in accuracy and readability.