Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

ABSTRACT: Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aimed to develop stroke prediction models, and urban-rural subgroup analyses were conducted to explore disparities in determinants among middle-aged and older adults. We employed nine machine learning algorithms, namely logistic regression (LR), adaptive boosting classifier, support vector machines, extreme gradient boosting, random forest, Gaussian naive Bayes (GNB), gradient boosting machine, light gradient boosting decision machine, and K Nearest Neighbours, using data derived from 9,413 individuals aged 45 years and above obtained from the Chi

SUBMITTER: Zhu J 

PROVIDER: S-EPMC11861258 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature