Author/Authors :
Shao, Zeguo School of Medical Instrumentation - Shanghai University of Medicine &Health Sciences - Shanghai, China , Xiang, Yuhong School of Medical Instrumentation - Shanghai University of Medicine &Health Sciences - Shanghai, China , Zhu, Yingchao Nursing Department - Shanghai Pudong New District Zhoupu Hospital - Shanghai, China , Fan, Aiqin Pudong New Area Lingqiao Community Health Service Center - Shanghai, China , Zhang, Peng School of Clinical Medicine - Shanghai University of Medicine & Health Sciences - Shanghai, China
Abstract :
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.
