AEC Classifier: A Tree-Based Classifier with Error Control for Medical Disease Diagnosis and Other Applications

Wasif Bokhari, Ajay Bansal

Research output: Contribution to journalArticlepeer-review


In medical disease diagnosis, the cost of a false negative could greatly outweigh the cost of a false positive. This is because the former could cost a life, whereas the latter may only cause medical costs and stress to the patient. The unique nature of this problem highlights the need of asymmetric error control for binary classification applications. In this domain, traditional machine learning classifiers may not be ideal as they do not provide a way to control the number of false negatives below a certain threshold. This paper proposes a novel tree-based binary classification algorithm that can control the number of false negatives with a mathematical guarantee, based on Neyman-Pearson (NP) Lemma. This classifier is evaluated on the data obtained from different heart studies and it predicts the risk of cardiac disease, not only with comparable accuracy and AUC-ROC score but also with full control over the number of false negatives. The methodology used to construct this classifier can be expanded to many more use cases, not only in medical disease diagnosis but also beyond as shown from analysis on different diverse datasets.

Original languageEnglish (US)
Pages (from-to)241-262
Number of pages22
JournalInternational Journal of Semantic Computing
Issue number2
StatePublished - Jun 2021


  • Asymmetric error control
  • binary classification
  • cardiovascular disease
  • machine learning
  • predictive analysis
  • tree-based classifiers

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Linguistics and Language
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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