Improved Heart Disease Prediction Using Particle Swarm Optimization Based Stacked Sparse Autoencoder

Heart disease is the leading cause of death globally. The most common type of heart disease is coronary heart disease, which occurs when there is a build-up of plaque inside the arteries that supply blood to the heart, making blood circulation difficult. The prediction of heart disease is a challenge in clinical machine learning. Early detection of people at risk of the disease is vital in preventing its progression. This paper proposes a deep learning approach to achieve improved prediction of heart disease. An enhanced stacked sparse autoencoder network (SSAE) is developed to achieve efficient feature learning. The network consists of multiple sparse autoencoders and a softmax classifier. Additionally, in deep learning models, the algorithm's parameters need to be optimized appropriately to obtain efficient performance. Hence, we propose a particle swarm optimization (PSO) based technique to tune the parameters of the stacked sparse autoencoder. The optimization by the PSO improves the feature learning and classification performance of the SSAE. Meanwhile, the multilayer architecture of autoencoders usually leads to internal covariate shift, a problem that affects the generalization ability of the network; hence, batch normalization is introduced to prevent this problem. The experimental results show that the proposed method effectively predicts heart disease by obtaining a classification accuracy of 0.973 and 0.961 on the Framingham and Cleveland heart disease datasets, respectively, thereby outperforming other machine learning methods and similar studies.

Automated summary

This study proposes a deep learning approach to improve the prediction of heart disease. An enhanced stacked sparse autoencoder network (SSAE) is used for efficient feature learning. The authors also introduce a particle swarm optimization (PSO) based technique to optimize the parameters of the autoencoder network, resulting in improved feature learning and classification performance. Experimental results demonstrate that the proposed method achieves a classification accuracy of 0.973 and 0.961 on the Framingham and Cleveland heart disease datasets, outperforming other machine learning methods and similar studies.