Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine
The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flo...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425907/ https://www.ncbi.nlm.nih.gov/pubmed/37588610 http://dx.doi.org/10.1016/j.heliyon.2023.e18832 |
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author | Lu, Haoxuan Huang, Li Xie, Yanqing Zhou, Zhong Cui, Hanbin Jing, Sheng Yang, Zhuo Zhu, Decai Wang, Shiqi Bao, Donggang Liang, Guoxi Cai, Zhennao Chen, Huiling He, Wenming |
author_facet | Lu, Haoxuan Huang, Li Xie, Yanqing Zhou, Zhong Cui, Hanbin Jing, Sheng Yang, Zhuo Zhu, Decai Wang, Shiqi Bao, Donggang Liang, Guoxi Cai, Zhennao Chen, Huiling He, Wenming |
author_sort | Lu, Haoxuan |
collection | PubMed |
description | The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans. |
format | Online Article Text |
id | pubmed-10425907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104259072023-08-16 Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine Lu, Haoxuan Huang, Li Xie, Yanqing Zhou, Zhong Cui, Hanbin Jing, Sheng Yang, Zhuo Zhu, Decai Wang, Shiqi Bao, Donggang Liang, Guoxi Cai, Zhennao Chen, Huiling He, Wenming Heliyon Research Article The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans. Elsevier 2023-08-02 /pmc/articles/PMC10425907/ /pubmed/37588610 http://dx.doi.org/10.1016/j.heliyon.2023.e18832 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Lu, Haoxuan Huang, Li Xie, Yanqing Zhou, Zhong Cui, Hanbin Jing, Sheng Yang, Zhuo Zhu, Decai Wang, Shiqi Bao, Donggang Liang, Guoxi Cai, Zhennao Chen, Huiling He, Wenming Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
title | Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
title_full | Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
title_fullStr | Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
title_full_unstemmed | Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
title_short | Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
title_sort | prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425907/ https://www.ncbi.nlm.nih.gov/pubmed/37588610 http://dx.doi.org/10.1016/j.heliyon.2023.e18832 |
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