Purpose. To explore the influences of smoking, alcohol consumption, drinking tea, diet, sleep, and exercise on the risk of stroke and relationships among the factors, present corresponding knowledge-based rules, and provide a scientific basis for assessment and intervention of risk factors of stroke. Methods. The decision tree C4.5 algorithm was optimized and utilized to establish a model for stroke risk assessment; then, the main risk factors of stroke (including hypertension, dyslipidemia, diabetes, atrial fibrillation, body mass index (BMI), history of stroke, family history of stroke, and transient ischemic attack (TIA)) and daily habits (e.g., smoking, alcohol consumption, drinking tea, diet, sleep, and exercise) were analyzed; corresponding knowledge-based rules were finally presented. Establish a correlation matrix of stroke risk factors and analyze the relationship between stroke risk factors. Results. The accuracy of the established model for stroke risk assessment was 87.53%, and the kappa coefficient was 0.8344, which was superior to that of the random forest and Logistic algorithm. Additionally, 37 knowledge-based rules that can be used for prevention of risk factors of stroke were derived and verified. According to in-depth analysis of risk factors of stroke, the values of smoking, exercise, sleep, drinking tea, alcohol consumption, and diet were 6.00, 7.00, 8.67, 9.33, 10.00, 10.60, and 10.75, respectively, indicating that their influence on risk factors of stroke was reduced in turn; on the one hand, smoking and exercise were strongly associated with other risk factors of stroke; on the other hand, sleep, drinking tea, alcohol consumption, and diet were not firmly associated with other risk factors of stroke, and they were relatively tightly associated with smoking and exercise. Conclusions. Establishment of a model for stroke risk assessment, analysis of factors influencing risk factors of stroke, analysis of relationships among those factors, and derivation of knowledge-based rules are helpful for prevention and treatment of stroke.

中文翻译：

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基于决策树和相关矩阵的日常生活习惯对中风危险因素的影响。

目的。探讨吸烟、饮酒、饮茶、饮食、睡眠、运动对脑卒中风险的影响及各因素之间的关系，提出相应的知识化规则，为脑卒中风险因素的评估和干预提供科学依据。中风。方法. 优化决策树C4.5算法，建立脑卒中风险评估模型；然后，中风的主要危险因素（包括高血压、血脂异常、糖尿病、心房颤动、体重指数（BMI）、中风史、中风家族史和短暂性脑缺血发作（TIA））和日常习惯（如吸烟） 、饮酒、饮茶、饮食、睡眠、运动）进行分析；最后提出了相应的基于知识的规则。建立卒中危险因素相关矩阵，分析卒中危险因素之间的关系。结果. 建立的脑卒中风险评估模型准确率为87.53%，kappa系数为0.8344，优于随机森林和Logistic算法。此外，还得出并验证了 37 条可用于预防中风危险因素的基于知识的规则。根据对脑卒中危险因素的深入分析，吸烟、运动、睡眠、喝茶、饮酒、饮食的值分别为6.00、7.00、8.67、9.33、10.00、10.60和10.75，表明他们对卒中危险因素的影响依次降低；一方面，吸烟和运动与中风的其他危险因素密切相关；另一方面，睡眠、喝茶、饮酒和饮食与中风的其他危险因素并没有密切相关，结论。建立卒中风险评估模型，分析影响卒中危险因素的因素，分析各因素之间的关系，推导出基于知识的规则，有助于卒中的防治。