Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

Yanqing Xie,Mingjing Wang,Haoxuan Lu,Huiling Chen,Wenming He

doi:10.3390/sym12101651

Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

Yanqing Xie, Mingjing Wang + Show 3 more

Open Access

https://doi.org/10.3390/sym12101651

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Journal: Symmetry	Publication Date: Oct 9, 2020
Citations: 12	License type: CC BY 4.0

Affiliation: Ningbo University, Duy Tan University, Wenzhou University

Abstract

To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.

Highlights

Coronary atherosclerotic heart disease (CHD), coronary heart disease, is a common cardiovascular disease (CVD) [1]
space transformation mechanism into salp swarm algorithm (STSSA), salp swarm algorithm (SSA), PSO, and GWO were implemented from scratch
Regarding the random forest (RF), kernel extreme learning machine (KELM), and support vector machine (SVM), the related codes shared in public websites

Summary

Introduction

Coronary atherosclerotic heart disease (CHD), coronary heart disease, is a common cardiovascular disease (CVD) [1]. CHD is caused by abnormal lipid metabolism. The abnormal deposition of lipids and other substances in the blood in the coronary artery can gradually develop into atherosclerotic plaques and cause stenosis and occlusion in the vascular lumen, leading to ischemia, hypoxia, or necrosis of cardiomyocytes in the corresponding blood supply area, and cause clinical symptoms such as chest tightness and chest pain. The incidence of CVD has been increasing annually and became the biggest threat to human health. Studies have shown that the prevalence of cardiovascular diseases is still on the rise. According to an American Heart Association (AHA)

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