应用人工神经网络预测个体患原发性高血压病危险度

The application of artificial neural networks to predict individual risk of essential hypertension

目的 建立个体患原发性高血压病的预测模型，评价并探讨预测个体患病的新方法。方法 选择3054名社区居民流行病学调查资料，按照年龄、性别均衡性，按4：1分为训练集（2438名）与检验集（616名）两部分，分别用于筛选变量、建立预测模型及时模型的检测和评价。应用人工神经网络（ANN）和logistic回归分析方法建立模型，用ROC方法评价所建立的高血压患病预测模型的优劣。结果 对616名检验集预测，ANN模型的特异性较低，但准确性、灵敏度指标均优于logistic回归模型，ANN，的约登指数为0．8399，明显高于其他两个模型；通过ROC曲线下面积比较模型的预测能力：logistic回归方法曲线下面积（Az=0．732±0．026）小于ANN模型（ANN2和ANN1分别为0．918±0．013、0．900±0．014），即ANN模型有更好的预测判别效能。结论 初步证明在预测个体患高血压病方面，ANN方法预测效能更优，从而为解决个体发病危险预测提供了一个新方法。

Objective To establish models to predict individual risk of essential hypertension and to evaluate and explore new forecasting methods. Methods To select data of 3054 community residents from a epidemiological survey and divided them into 4：1 （2438 cases and 616 cases） ratio in accordance with the balance of age and sex to filter variables, and to establish, test and evaluate the prediction models. Using artificial neural network （ANN） and logistic regression analysis to establish models while applying ROC to evaluate the prediction models. Results Forecast results of the models applying to the test set proved that ANN had lower specificity but better veracity and sensitivity than logistic regression. In particular, the Youden＇s index of the ANN2 came up to 0. 8399 which was distinctly higher than the other two models. When the area was under the ROC curve of logistic regression, the ANN1 and ANN2 models equaled to 0. 732 ± 0. 026,0. 900 ± 0. 014 and 0. 918 ± 0.013 respectively, which proved that the ANN model was better in the prediction about individual health risk of essential hypertension. Conclusion Our results showed that ANN method seemed better than logistic regression in terms of predicting the individual risk from hypertension thus supplied a new method to solve the forecast of individual risk.