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In China, stroke is the first leading cause of death in recent years. It is amajor cause of long-term physical and cognitive impairment, which bring greatpressure on the National Public Health System. Evaluation of the risk ofgetting stroke is important for the prevention and treatment of stroke inChina. A data set with 2000 hospitalized stroke patients in 2018 and 27583residents during the year 2017 to 2020 is analyzed in this study. Due to dataincompleteness, inconsistency, and non-structured formats, missing values inthe raw data are filled with -1 as an abnormal class. With the cleanedfeatures, three models on risk levels of getting stroke are built by usingmachine learning methods. The importance of "8+2" factors from China NationalStroke Prevention Project (CSPP) is evaluated via decision tree and randomforest models. Except for "8+2" factors the importance of features and SHAP1values for lifestyle information, demographic information, and medicalmeasurement are evaluated and ranked via a random forest model. Furthermore, alogistic regression model is applied to evaluate the probability of gettingstroke for different risk levels. Based on the census data in both communitiesand hospitals from Shanxi Province, we investigate different risk factors ofgetting stroke and their ranking with interpretable machine learning models.The results show that Hypertension (Systolic blood pressure, Diastolic bloodpressure), Physical Inactivity (Lack of sports), and Overweight (BMI) areranked as the top three high-risk factors of getting stroke in Shanxi province.The probability of getting stroke for a person can also be predicted via ourmachine learning model.

Analysis and classification of main risk factors causing stroke in Shanxi Province