Construction of a New Smooth Support Vector Machine Model and its Application in Heart Disease Diagnosis
Jianjian Wang, Feng He, Shouheng Sun
Abstract
Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can’t be used to find the solution. Firstly, to overcome the non-smooth property of this model, a new padé33 approximation smooth function is constructed by rational approximation method, and a new smooth support vector machine model (SSVM) is established based on the smooth function.
Introduction
With the development of machine learning, support vector machine (SVM) is a new machine learning method in statistical learning theory, and has achieved remarkable results in face recognition, population prediction, image retrieval, data mining and other fields. SVM has good generalization performance, and its classifier shows special advantages in solving the pattern recognition problems of small samples, nonlinearity and high dimension, especially in dealing with classification problems.
Acknowledgments:
This work was supported by the General Social Science Project of Beijing Municipal Education Commission [grant number SM202210037008] and the School level youth fund project of Beijing Wuzi University [grant number 2022XJQN35]. We would like to thank everyone who provided the materials included in this study. Additionally, we express our appreciation to Associate Professor Sun f or his valuable contribution in the English translation and editing of this work. We also thank anonymous reviewers for their constructive comments and suggestions.
Citation: Wang J, He F, Sun S (2023) Construction of a new smooth support vector machine model and its application in heart disease diagnosis. PLoS ONE 18(2): e0280804. https://doi.org/10.1371/journal.pone.0280804
Editor: Fucai Lin, Minnan Normal University, CHINA
Received: July 21, 2022; Accepted: December 19, 2022; Published: February 9, 2023.
Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results presented in the study are available from the following website https://archive.ics.uci.edu/ml/index.php.
Funding: This work was supported by the General Social Science Project of Beijing Municipal Education Commission [grant number SM202210037008] and the School level youth fund project of Beijing Wuzi University [grant number 2022XJQN35]. The funders played a role in data collection and analysis, decision on publication and preparation of manuscripts.
Competing interests: The authors have declared that no competing interests exist.
