Novel Insights on Establishing Machine Learning-Based Stroke Prediction Models Among Hypertensive Adults.

ABSTRACT:

Background

Stroke is a major global health burden, and risk prediction is essential for the primary prevention of stroke. However, uncertainty remains about the optimal prediction model for analyzing stroke risk. In this study, we aim to determine the most effective stroke prediction method in a Chinese hypertensive population using machine learning and establish a general methodological pipeline for future analysis.

Methods

The training set included 70% of data (n = 14,491) from the China Stroke Primary Prevention Trial (CSPPT). Internal validation was processed with the rest 30% of CSPPT data (n = 6,211), and external validation was conducted using a nested case-control (NCC) dataset (n = 2,568). The primary outcome was the first stroke. Four received

SUBMITTER: Huang X 

PROVIDER: S-EPMC9120532 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature