Abstract Background: The occurrence of stroke in patients with chronic obstructive pulmonary disease (COPD) can have potentially devastating consequences; however, there is still a lack of predictive models that accurately predict the risk of stroke in community-based COPD patients in China. The aim of this study was to construct a novel predictive model that accurately predicts the predictive model for the risk of stroke in community-based COPD patients by applying a machine learning methodology within the Chinese community. Methods: The clinical data of 809 Community COPD patients were analyzed by using the 2020 China Health and Retirement Longitudinal Study (CHARLS) database. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze predictors. Multiple machine learning (ML) classification models are integrated to analyze and identify the optimal model, and Shapley Additive exPlanations (SHAP) interpretation was developed for personalized risk assessment.Results:The following six variables:Heart_disease,Hyperlipidemia,Hypertension,ADL_score, Cesd_score and Parkinson are predictors of stroke in community-based COPD patients. Logistic classification model was the optimal model, test set area under curve (AUC) (95% confidence interval, CI):0.913 (0.835-0.992), accuracy: 0.823, sensitivity: 0.818, and specificity: 0.823. Conclusions: The model constructed in this study has relatively reliable predictive performance, which helps clinical doctors identify high-risk populations of community COPD patients prone to stroke at an early stage.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Not Applicable

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The research that included human participants underwent review and approval by CHARLS, as it was ethically approved by the Ethics Review Board of Peking University with (approval number IRB00001052-11015). Each participant provided their consent by signing an informed consent form. This study did not require written informed consent for participation as per the national laws and institutional regulations.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Not Applicable

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Not Applicable

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Not Applicable

The datasets utilized in this study can be obtained from the corresponding author upon submission of a reasonable request.