目的 探讨和评价决策树与Logistic回归用于预测高血压患者健康素养中的可行性与准确性.方法 利用Logistic回归分析和Answer Tree软件分别建立高血压患者健康素养预测模型,利用受试者工作曲线(ROC)评价两个预测模型的优劣.结果 Logistic回归预测模型的灵敏度(82.5％)、Youden指数(50.9％)高于决策树模型(77.9％,48.0％),决策树模型的特异性(70.1％)高于Logistic回归预测模型(68.4％),误判率(29.9％)低于Logistic回归预测模型(31.6％);决策树模型ROC曲线下面积与Logistic回归预测模型ROC曲线下面积相当(0.813 vs 0.847).结论 利用决策树预测高血压患者健康素养效果与Logistic回归模型相当,根据决策树模型可以确定高血压患者健康素养筛选策略,数据挖掘技术可以用于慢性病患者健康素养预测中.%Objective To study and evaluate the feasibility and accuracy for the application of decision tree methods and logistic regression on the health literacy prediction of hypertension patients. Method Two health literacy prediction models were generated with decision tree methods and logistic regression respectively. The receiver operating curve ( ROC) was used to evaluate the results of the two prediction models. Result The sensitivity(82. 5%) , Youden index (50. 9%)by logistic regression model was higher than decision tree model(77. 9% ,48. 0%) , the Spe-cificity(70. 1%)by decision tree model was higher than that of logistic regression model(68. 4%), The error rate (29.9%) was lower than that of logistic regression model(31. 6%). The ROC for both models were 0. 813 and 0. 847. Conclusion The effect of decision tree prediction model was similar to logistic regression prediction model. Health literacy screening strategy could be obtained by decision tree prediction model, implying the data mining methods is feasible in the chronic disease management of community health service.
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