Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants.

ABSTRACT:

Objective

To develop an optimal screening model to identify the individuals with a high risk of hypertension in China by comparing tree-based machine learning models, such as classification and regression tree, random forest, adaboost with a decision tree, extreme gradient boosting decision tree, and other machine learning models like an artificial neural network, naive Bayes, and traditional logistic regression models.

Methods

A total of 4,287,407 adults participating in the national physical examination were included in the study. Features were selected using the least absolute shrinkage and selection operator regression. The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in comb

SUBMITTER: Ji W 

PROVIDER: S-EPMC9548597 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature