基于改进深度神经网络的心血管疾病预测

心血管疾病是威胁人类健康的常见疾病，为了能够更加准确地对其预测，本文在传统DNN模型基础上进行优化改进，提出定向正则的深度神经网络(TR-DNN)模型，通过改进原有深度神经网络模型所存在的缺陷，使其能够更好地对心血管疾病数据集进行训练并测试，进一步实现心血管疾病预测任务。实验表明该模型在数据集训练上的表现良好，并且在测试集上取得优秀的结果。最后，将TR-DNN与SVM、RF、XGBoost模型在同一数据集进行结果比较，TR-DNN模型的各项评价指标均优于其它模型，在准确率方面相较传统DNN模型提高1.507个百分点，召回率提高1.57个百分点，特异度提高2.54个百分点，精确率提高1.51个百分点。因此，TR-DNN模型可以应用于心血管疾病的预测。

Prediction of Cardiovascular Disease Based on Improved Deep Neural Network

Cardiovascular disease is a common disease threatening human health. In order to predict it more accurately, this paper optimizes and improves the traditional DNN model and proposes a directional regular deep neural network (TR-DNN) model. By improving the defects of the original deep neural network model, it can better train and test the cardiovascular disease data set, further realize the task of cardiovascular disease prediction. Experiments show that the model performs well in data set training, and achieves excellent results in test set. Finally, comparing the results of TR-DNN with SVM, RF and XGBoost models in the same data set, the evaluation indexes of TR-DNN model are better than other models. Compared with the traditional DNN model, TR-DNN model improves the accuracy by 1.507 percentage points, the recall by 1.57 percentage points, the specificity by 2.54 percentage points and the precision by 1.51 percentage points. Therefore, TR-DNN model can be applied to the prediction of cardiovascular disease.