Deep Autoencoder Based Neural Networks for Coronary Heart Disease Risk Prediction

摘要

The World Health Organization (WHO) reported that coronary heart disease (CHD) is one of the top causes of global mortality, and it is also highly ranked in Korea. The wrong lifestyle such as alcohol, tobacco, and high fatty food is directly involved in the main risk factors for CHD. In the early stage, it is possible to prevent suffering from CHD by an appropriate drug and healthy lifestyle which lead to effective treatment. In this paper, we propose a deep autoencoder based neural networks (DAE-NNs) to predict CHD risk. First, a dataset is divided into two groups by their divergence using a deep autoencoder model. Then, deep neural network (NN) classifiers are trained on each group of dataset separately. As a result, the performance measurements including accuracy, F-measure and AUC score reached 83.53%, 84.36%, and 84.02%, respectively in the Korean population. These results show that our proposed DAE-NNs approach outperformed typical data mining based classifiers for CHD risk prediction.