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

ABSTRACT:

Objective

Tuberculosis remains a major global health challenge, with delayed diagnosis contributing to increased transmission and disease burden. While microbiological tests are the gold standard for confirming active tuberculosis, many cases lack microbiological evidence, necessitating additional clinical and laboratory data for diagnosis. The complete blood count (CBC), an inexpensive and widely available test, could provide a valuable tool for tuberculosis diagnosis by analyzing disturbances in blood parameters. This study aimed to develop and evaluate a machine learning (ML)-based web application, TubIAgnosis, for diagnosing active tuberculosis using CBC data.

Methods

We conducted a retrospective case-control study using data from 449 tuberculosis patients and 1200 healt

SUBMITTER: Ghermi M 

PROVIDER: S-EPMC11367613 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature