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

Yuping Li,Jionghui Jiang,Xi’an Feng,Chengye Li,Hui Huang,Huiling Chen,Suying Zhou

doi:10.1186/s12859-019-2771-z

Abstract

BackgroundIt 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.ResultsIn 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.ConclusionsThe 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.

Highlights

It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer
A 10-fold cross-validation method was used on data to make detailed comparison between Levy flight enhanced FOA (LFOA)--support vector machine (SVM), fly optimization algorithm (FOA)-SVM, GA-SVM, SVM model based on original PSO (PSO-SVM), random forest (RF), back propagation neural network (BPNN) and SVM
During the inner parameter optimization procedure, the SVM parameters are dynamically adjusted by the LFOA technique via the 5-fold cross validation (CV) analysis

Summary

Introduction

It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. An enhanced machine learning framework is established to diagnose the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. Kaya et al [13] proposed a novel approach based on rough set and extreme learning machine for distinguishing the benign or malignant breast cancer. Akay et al [14] proposed a novel SVM combined with feature selection for breast cancer diagnosis. Accurate pathological diagnosis of breast cancer depends on features, which are extracted from histopathology images. There are a lot of works for diagnosis of breast cancer based on histopathology images’ features

Methods

Results

Conclusion