دورية أكاديمية
Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine
العنوان: | Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine |
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المؤلفون: | Haoxuan Lu, Li Huang, Yanqing Xie, Zhong Zhou, Hanbin Cui, Sheng Jing, Zhuo Yang, Decai Zhu, Shiqi Wang, Donggang Bao, Guoxi Liang, Zhennao Cai, Huiling Chen, Wenming He |
المصدر: | Heliyon, Vol 9, Iss 8, Pp e18832- (2023) |
بيانات النشر: | Elsevier, 2023. |
سنة النشر: | 2023 |
المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
مصطلحات موضوعية: | Coronary heart disease, Fractional flow reserve, Ant lion optimizer, Global optimization, Support vector machine, Feature selection, Science (General), Q1-390, Social sciences (General), H1-99 |
الوصف: | 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. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2405-8440 |
Relation: | http://www.sciencedirect.com/science/article/pii/S2405844023060401; https://doaj.org/toc/2405-8440 |
DOI: | 10.1016/j.heliyon.2023.e18832 |
URL الوصول: | https://doaj.org/article/57404d9f68bd46e28fe6b455691fe058 |
رقم الأكسشن: | edsdoj.57404d9f68bd46e28fe6b455691fe058 |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 24058440 |
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DOI: | 10.1016/j.heliyon.2023.e18832 |