Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia.

ABSTRACT:

Background and objectives

Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia.

Materials and methods

The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the sele

SUBMITTER: Islam MM 

PROVIDER: S-EPMC10449142 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature