TY - JOUR A2 - Xiaoqing Gu AU - Zhang, deng - Chen AU - Chen, Yunyi AU - Chen, Yuxuan AU - Ye, Shengyi AU - Cai, Wenyu AU - Jiang, Junxue AU - Xu, Yechuan AU - Zheng, Gongfeng AU - Chen，基于嵌入式特征选择方法和深度神经网络的心脏病预测因此，通过早期定期体检获得的一些简单的身体指标来预测心脏病已成为一项有价值的课题。在临床上，对这些与心脏病相关的指标的敏感性是预测并为进一步诊断提供可靠依据的关键。然而，由于数据量大，手工分析和预测工作量大、难度大。我们的研究目的是通过身体的各种指标准确、快速地预测心脏病。本文提出了一种新的心脏病预测模型。提出了一种将嵌入特征选择方法与深度神经网络相结合的心脏病预测算法。这种嵌入式特征选择方法基于线性svc算法，使用L1范数作为惩罚项来选择与心脏病显著相关的特征子集。这些特征被输入到我们构建的深度神经网络中。 The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease. SN - 2040-2295 UR - https://doi.org/10.1155/2021/6260022 DO - 10.1155/2021/6260022 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -