Feature selection and prediction of treatment failure in tuberculosis.

ABSTRACT:

Background

Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control.

Objective

To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.

Methods

On a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone.

Results

The complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75

SUBMITTER: Sauer CM 

PROVIDER: S-EPMC6245785 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature