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Cited by 50Artificial Intelligence in HealthcareAI in cancer detectionMetaheuristic Optimization Algorithms Research

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Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

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Kamel, Seyed Reza, YaghoubZadeh, Reyhaneh, Kheirabadi, Maryam·2019·Journal of Big Data
DOI 10.1186/s40537-019-0247-7
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Abstract

One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.

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Research works printed but not linked: 21

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Metadata

Authors
Kamel, S. R., YaghoubZadeh, R., & Kheirabadi, M.
Affiliations
  • Islamic Azad University, Mashhad (IR)
  • Islamic Azad University, Tehran (IR)
Corresponding author
Kamel, Seyed Reza
ORCID
Venue
Journal of Big Data
Publisher
Springer Science and Business Media LLC
Published
2019-10-16
Volume
6
Issue
1
ISSN
2196-1115 (ISSN-L)
DOI
10.1186/s40537-019-0247-7
OpenAlex
W2981089454
Cited by
50
References
0
Citations by year
2026: 2 · 2025: 3 · 2024: 9 · 2023: 15 · 2022: 7 · 2021: 7 · 2020: 7
FWCI
5.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal Of Big Data (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Keywords
breast cancer diagnosis, support vector machine, grey wolf optimizer, feature selection, early diagnosis, medical image classification, classification accuracy

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CoChat. (2026). Citation check: Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer. https://app.cochat.ai/papers/10.1186/s40537-019-0247-7
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Kamel, S. R., YaghoubZadeh, R., & Kheirabadi, M. (2019). Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer. Journal of Big Data. https://doi.org/10.1186/s40537-019-0247-7