OBJECTIVE: To determine the major risk factors and their interactions of ischemic stroke (IS) and to develop a classification tree model to predict the incidence risk of IS for a Chinese population. METHODS: Exhaustive Chi-squared Automatic Interaction Detection (Exhaustive CHAID) algorithm of classification tree method was applied to build a prediction model for the incidence risk of IS under the design of 1:1 matched case-control study. The statistics of misclassification risk was used to evaluate the fitness of the model. RESULTS: In the prediction model, six variables of physical exercise, history of hypertension, tea drinking, HDL-c level, smoking status and educational level were in turn selected as the predictors of IS incidence risk. In the subgroup of lacking of physical exercise, individuals who had history of hypertension would have a significantly higher IS risk (92%) than that of the ones who had no history of hypertension (64%). The misclassification risk estimate of the prediction model was 0.21 with the standard error of 0.02, indicating that 79% of the cases could be classified correctly based on current prediction model. CONCLUSIONS: Lacking of physical exercise and history of hypertension are identified to be the prominent predicting variables of IS risk for a hospital population of southern China. Although CHAID analysis could provide detailed information and insight about interactions among risk factors of IS, we still need to validate our model and improve the vascular risk prediction for Chinese subjects in further studies.
OBJECTIVE: To determine the major risk factors and their interactions of ischemic stroke (IS) and to develop a classification tree model to predict the incidence risk of IS for a Chinese population. METHODS: Exhaustive Chi-squared Automatic Interaction Detection (Exhaustive CHAID) algorithm of classification tree method was applied to build a prediction model for the incidence risk of IS under the design of 1:1 matched case-control study. The statistics of misclassification risk was used to evaluate the fitness of the model. RESULTS: In the prediction model, six variables of physical exercise, history of hypertension, tea drinking, HDL-c level, smoking status and educational level were in turn selected as the predictors of IS incidence risk. In the subgroup of lacking of physical exercise, individuals who had history of hypertension would have a significantly higher IS risk (92%) than that of the ones who had no history of hypertension (64%). The misclassification risk estimate of the prediction model was 0.21 with the standard error of 0.02, indicating that 79% of the cases could be classified correctly based on current prediction model. CONCLUSIONS: Lacking of physical exercise and history of hypertension are identified to be the prominent predicting variables of IS risk for a hospital population of southern China. Although CHAID analysis could provide detailed information and insight about interactions among risk factors of IS, we still need to validate our model and improve the vascular risk prediction for Chinese subjects in further studies.