Screening hypertension using non-laboratory risk factors with machine learning: a retrospective cross-sectional study in Indonesia.

ABSTRACT:

Objective

This study aimed to screen for hypertension in a vast Indonesian population using machine learning (ML) and 11 non-laboratory risk factors, validating the results through internal and external validations.

Setting and participants

From the initial 1 782 365 participants aged 15 and above registered at the Integrated Counseling Post primary care centres across Indonesia from 2014 to 2017, incomplete data and outliers were excluded, and 268 210 participants were included in our analysis. The dataset was split deterministically into a dataset for training using 10-fold internal cross-validation of 204 315 participants and another dataset for external validation of 63 895 participants.

Design

This retrospective cross-sectional study used three ML algorithms, tha

SUBMITTER: Estiko RI 

PROVIDER: S-EPMC12406909 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature