دورية أكاديمية
A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features
| العنوان: | A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features |
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| المؤلفون: | Hui Huang, Xi’an Feng, Suying Zhou, Jionghui Jiang, Huiling Chen, Yuping Li, Chengye Li |
| المصدر: | BMC Bioinformatics, Vol 20, Iss S8, Pp 1-14 (2019) |
| بيانات النشر: | BMC, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| مصطلحات موضوعية: | Support vector machine, Parameter optimization, Fruit fly optimization, Levy flight, Breast cancer diagnosis, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| الوصف: | Abstract Background It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. Results In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. Conclusions The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2105 |
| Relation: | http://link.springer.com/article/10.1186/s12859-019-2771-z; https://doaj.org/toc/1471-2105 |
| DOI: | 10.1186/s12859-019-2771-z |
| URL الوصول: | https://doaj.org/article/d4d6fd9e09c24410baaf09e5627e4087 |
| رقم الأكسشن: | edsdoj.4d6fd9e09c24410baaf09e5627e4087 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14712105 |
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| DOI: | 10.1186/s12859-019-2771-z |