What we found on our way to building a classifier: A critical analysis of the AHA screening questionnaire

Quazi Abidur Rahman, Sivajothi Kanagalingam, Aurelio Pinheiro, Theodore Abraham, Hagit Shatkay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


The American Heart Association (AHA) has recommended a 12-element questionnaire for pre-participation screening of athletes, in order to reduce and hopefully prevent sudden cardiac death in young athletes. This screening procedure is widely used throughout the United States, but its efficacy for discriminating Normal from Non-normal heart condition is unclear. As part of a larger study on cardiovascular disorders in young athletes, we set out to train machine-learning-based classifiers to automatically categorize athletes into risk-levels based on their answers to the AHA-questionnaire. We also conducted information-based and probabilistic analysis of each question to identify the ones that may best predict athletes' heart condition. However, surprisingly, the results indicate that the AHA-recommended screening procedure itself does not effectively distinguish between Normal and Non-normal heart as identified by cardiologists using Electro- and Echo-cardiogram examinations. Our results suggest that ECG and Echo, rather than the questionnaire, should be considered for screening young athletes.

Original languageEnglish (US)
Title of host publicationBrain and Health Informatics - International Conference, BHI 2013, Proceedings
Number of pages12
StatePublished - 2013
Externally publishedYes
EventInternational Conference on Brain and Health Informatics, BHI 2013 - Maebashi, Japan
Duration: Oct 29 2013Oct 31 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8211 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Brain and Health Informatics, BHI 2013

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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