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

Modelling oculomotor data with decision tree induction.

K Viikki1, E Isotalo, M Juhola

  • 1Department of Computer Science, University of Tampere, Finland. kv@cs.uta.fi

Studies in Health Technology and Informatics
|March 21, 2000
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

Imaging of Temporal Bone.

Advances in oto-rhino-laryngology·2019
Same author

Positive impact of Ménière's disorder on significant others as well as on patients: our experience from eighty-eight respondents.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery·2013
Same author

How one hundred and eighty three people with Menière's disorder relieve their symptoms: a random cohort questionnaire study.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery·2013
Same author

Tinnitus complaint behaviour in long-standing Menière's disorder: its association with the other cardinal symptoms.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery·2011
Same author

Twelve-month clinical follow-up study of voice patients' recovery using the Voice Activity and Participation Profile (VAPP).

Journal of voice : official journal of the Voice Foundation·2010
Same author

Application of a modified two-point backward difference to sequential event detection in surface electromyography.

Journal of medical engineering & technology·2009
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Decision tree induction effectively classifies lesions affecting the cerebello-pontine angle and brainstem using oculomotor test data. This method proves useful for real-world medical research despite data challenges.

Area of Science:

  • Neurology
  • Ophthalmology
  • Medical Informatics

Background:

  • Accurate localization of neurological lesions is crucial for effective treatment.
  • Oculomotor tests provide valuable diagnostic information for brainstem and cerebello-pontine angle pathologies.
  • Existing classification methods face challenges with real-world medical data, including missing values and limited case numbers.

Purpose of the Study:

  • To investigate the relationship between oculomotor test results and lesion sites in various neurological conditions.
  • To develop and evaluate classification models using decision tree induction for lesion localization.
  • To assess the utility of decision tree induction in handling complex, real-world medical datasets.

Main Methods:

  • Utilized a dataset comprising patients with cerebello-pontine angle tumors, hemangioblastomas, cerebello-brainstem infarctions, Meniere's disease, and control subjects.

Related Experiment Videos

  • Applied decision tree induction algorithms to generate classification models.
  • Evaluated model performance and parameter combinations for efficient classification, addressing data scarcity and missing values.

    • Main Results:

    • Decision tree induction models demonstrated effectiveness in classifying lesion sites based on oculomotor test results.
    • The method proved robust in handling datasets with missing values and a limited number of cases.
    • Generated models, when reviewed by an expert physician, provided potentially beneficial research insights.

    Conclusions:

    • Decision tree induction is a viable and useful method for classifying neurological lesions using oculomotor data, even with imperfect real-world datasets.
    • The study highlights the potential of machine learning approaches in neurological research and diagnosis.
    • Expert physician evaluation of decision tree models enhances their clinical relevance and research applicability.