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TubIAgnosis: A machine learning-based web application for active tuberculosis diagnosis using complete blood count

Mohamed Ghermi1,2, Meriam Messedi3, Chahira Adida2

  • 1Biology of Microorganisms and Biotechnology Laboratory, University of Oran1 Ahmed Ben Bella, Oran, Algeria.

Digital Health
|September 3, 2024
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Summary

A new machine learning tool, TubIAgnosis, uses complete blood count (CBC) data to help diagnose active tuberculosis. This accessible web application shows promise in improving tuberculosis diagnosis, especially in resource-limited areas.

Keywords:
Complete blood countartificial intelligencediagnosismachine learningtuberculosis

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Area of Science:

  • Medical Diagnostics
  • Machine Learning Applications
  • Hematology

Background:

  • Tuberculosis (TB) diagnosis is challenging, with microbiological tests often unavailable or inconclusive.
  • Delayed diagnosis of TB increases transmission and disease burden globally.
  • Complete blood count (CBC) offers an accessible, inexpensive method to identify potential diagnostic markers.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML)-based web application for active tuberculosis diagnosis using CBC data.
  • To assess the efficacy of ML algorithms in identifying TB from routine blood test parameters.
  • To create a user-friendly tool, TubIAgnosis, for improved TB diagnostic support.

Main Methods:

  • A retrospective case-control study included 449 TB patients and 1200 controls.
  • Eight ML algorithms were trained on 18 CBC parameters and demographic data.
  • Model performance was evaluated using balanced accuracy, sensitivity, specificity, and AUC.

Main Results:

  • The Extreme Gradient Boosting (XGB) model achieved 83.3% balanced accuracy and 89.4% AUC.
  • Platelet-to-lymphocyte ratio emerged as a key predictive parameter.
  • The best model was deployed as the free web application, TubIAgnosis.

Conclusions:

  • TubIAgnosis demonstrates effective performance in diagnosing active tuberculosis using CBC data.
  • This ML-powered tool offers a cost-effective complement to existing TB diagnostic methods.
  • Further prospective studies are recommended to validate and enhance the application's utility.