Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

作者：Dengqing Zhang, Yunyi Chen, Yuxuan Chen, Shengyi Ye, Wenyu Cai, Junxue Jiang, Yechuan Xu, Gongfeng Zheng, Ming Chen · 发表于：Journal of Healthcare Engineering · 年份：2021 · DOI：10.1155/2021/6260022 · 被引用次数：83 · 研究领域：Artificial Intelligence in Healthcare、Traditional Chinese Medicine Studies、Machine Learning in Healthcare

In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. 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...