A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features

• Published in: BMC bioinformatics Vol. 20; no. Suppl 8; pp. 290 - 14
• Main Authors: Huang, Hui, Feng, Xi’an, Zhou, Suying, Jiang, Jionghui, Chen, Huiling, Li, Yuping, Li, Chengye
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.06.2019; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Animals; Artificial intelligence; Automation; Back propagation; Back propagation networks; Bioinformatics; Biomedical and Life Sciences; Breast cancer; Breast cancer diagnosis; Breast Neoplasms - diagnosis; Computational Biology/Bioinformatics; Computer aided medical diagnosis; Computer Appl. in Life Sciences; Diagnosis; Drosophila melanogaster - physiology; Female; Fruit fly optimization; Fruits; Gene expression; Genetic algorithms; Health aspects; Histopathology; Humans; Learning algorithms; Levy flight; Life Sciences; Machine learning; Medical diagnosis; Methods; Microarrays; Neural networks; Neural Networks (Computer); Optimization algorithms; Parameter optimization; Particle swarm optimization; Performance measurement; Reproducibility of Results; Support Vector Machine; Support vector machines; China; United States > US; Wisconsin; Support vector machine; Levy flight; Parameter optimization; Fruit fly optimization; Breast cancer diagnosis
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-019-2771-z

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.