Asymmetric Error Control for Binary Classification in Medical Disease Diagnosis

Wasif Bokhari, Ajay Bansal

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

Abstract

In binary classification applications, such as medical disease diagnosis, the cost of one type of error could greatly outweigh the other enabling the need of asymmetric error control. Due to this unique nature of the problem, where one error greatly outweighs the other, traditional machine learning techniques, even with much improved accuracy, may not be ideal as they do not provide a way to control the false negatives below a certain threshold. To address this need, a classification algorithm that can control the false negatives to a certain threshold is proposed. The theoretical foundation for this algorithm is based on Neyman-Pearson (NP) Lemma, which is used to construct a novel tree-based classifier that enables asymmetric error control. This classifier is evaluated on the data obtained from the Framingham heart study and it predicts the risk of a ten-year cardiac disease, not only with improved accuracy and F1 score but also with full control over the number of false negatives. With an improved accuracy in predicting cardiac disease, this tree-based classifier with asymmetric error control can reduce the burden of cardiac disease in populations and potentially save a lot of human lives. The methodology used to construct this classifier can be expanded to many more use cases in medical disease diagnosis.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-32
Number of pages8
ISBN (Electronic)9781728187082
DOIs
StatePublished - Dec 2020
Event3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020 - Irvine, United States
Duration: Dec 9 2020Dec 11 2020

Publication series

NameProceedings - 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020

Conference

Conference3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020
Country/TerritoryUnited States
CityIrvine
Period12/9/2012/11/20

Keywords

  • Asymmetric error control
  • Binary Classification
  • Tree based classifier

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Information Systems and Management

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