A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran.

ABSTRACT:

Background

The increasing burden of hypertension in low- to middle-income countries necessitates the assessment of care coverage to monitor progress and guide future policies. This study uses an ensemble learning approach to evaluate hypertension care coverage in a nationally representative Iranian survey.

Methods

The data source was the cross-sectional 2016 Iranian STEPwise approach to risk factor surveillance (STEPs). Hypertension was based on blood pressure ≥140/90 mmHg, reported use of anti-hypertensive medications, or a previous hypertension diagnosis. The four steps of care were screening (irrespective of blood pressure value), diagnosis, treatment, and control. The proportion of patients reaching each step was calculated, and a random forest model was used to identify

SUBMITTER: Tavolinejad H 

PROVIDER: S-EPMC9491523 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature