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

Automatic classification of biomedical information when classification error is unknown.

A M Stoddard

    Statistics in Medicine
    |July 1, 1984
    PubMed
    Summary

    A new distribution-free method for classifying alpha 1-antitrypsin phenotypes automatically achieved 74% agreement. This approach is useful for complex classification tasks where standard methods may be unreliable.

    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

    Factors associated with successful tobacco use cessation among teachers in Bihar state, India: a mixed-method study.

    Health education research·2020
    Same author

    Scaling up a tobacco control intervention in low resource settings: a case example for school teachers in India.

    Health education research·2018
    Same author

    Tracking intervention delivery in the 'Tobacco-Free Teachers/Tobacco-Free Society' program, Bihar, India.

    Health education research·2015
    Same author

    Experiences recruiting Indian worksites for an integrated health protection and health promotion randomized control trial in Maharashtra, India.

    Health education research·2015
    Same author

    Impact of organizational policies and practices on workplace injuries in a hospital setting.

    Journal of occupational and environmental medicine·2014
    Same author

    Associations of diet behaviours and intention to eat healthily with tobacco use among motor freight workers.

    Public health·2009

    Area of Science:

    • Biochemistry
    • Medical Diagnostics
    • Computational Biology

    Background:

    • Accurate classification of serum protein phenotypes, such as alpha 1-antitrypsin, is crucial for diagnosing various medical conditions.
    • Conventional classification procedures may have limitations, especially when dealing with complex datasets or unknown class reliabilities.

    Purpose of the Study:

    • To introduce and evaluate a novel distribution-free method for the automatic classification of alpha 1-antitrypsin phenotypes.
    • To assess the performance of this new method against established classification procedures.

    Main Methods:

    • Development of a distribution-free algorithmic approach for phenotype classification.
    • Validation of the method using an independent testing sample, comparing results with a conventional classification procedure.

    Related Experiment Videos

    Main Results:

    • The automatic classification method demonstrated 74% agreement with the conventional procedure.
    • A Kappa value of 0.24 was achieved, indicating moderate agreement.

    Conclusions:

    • The proposed distribution-free method offers a viable alternative for automatic phenotype classification.
    • This methodology is particularly valuable for classification problems where the accuracy of existing methods is uncertain or data distributions are non-standard.