Breast Cancer Detection Based on Modified Harris Hawks Optimization and Extreme Learning Machine Embedded with Feature Weighting

Feng Jiang, Qiannan Zhu, Tianhai Tian

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

Computer-aided diagnosis (CAD) can assist doctors with clinical diagnosis and improve diagnosis accuracy and efficiency further. It is significative and valuable for cancer detection by using machine learning. In this paper, a hybrid model based on optimization algorithm and machine learning with feature weighting is carried out to detect breast cancer. Firstly, to surmount the limitation of nonlinear and imbalanced data distribution, we apply feature weighting (FW) based on K-Means to make benign and malignant samples more separate. Then Particle Swarm Optimization (PSO) is used to enhance searching ability of Harris Hawks Optimization (HHO). Moreover, the HHO optimized by PSO (PHHO) is employed to optimize Extreme Learning Machine (ELM). Finally, in order to verify the availability of our proposed FW-PHHO-ELM model, experiments are implemented on Wisconsin Diagnosis Breast Cancer (WDBC) data set. The results indicate that the proposed model can achieve 98.76%, 97.37% and 99.46% on accuracy, sensitivity and specificity respectively. The comparison results demonstrate that the proposed model outperformed reported benchmark models and existing models on accuracy and sensitivity. Besides, the proposed model could also balance sensitivity and specificity well.

Original languageEnglish
Pages (from-to)3631-3654
Number of pages24
JournalNeural Processing Letters
Volume55
Issue number4
DOIs
Publication statusPublished - Aug 2023

Keywords

  • Breast cancer detection
  • Extreme learning machine
  • Feature weighting
  • Harris hawks optimization
  • Particle swarm optimization

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