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

The DOI matches its Crossref record.

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

Open access
Kamel, Seyed Reza, YaghoubZadeh, Reyhaneh, Kheirabadi, Maryam·2019·Journal of Big Data

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.

Overview

Integrity signals

Registry

Crossref · 10.1186/s40537-019-0247-7

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 21

  1. Hamsagayathri P, Sampath P. Decision tree classifiers for classification of breast cancer. Int J Curr Pharm Res. 2017;9(2):31. · DOI
  2. Osman AH. An enhanced breast cancer diagnosis scheme based on two-step-SVM technique. Int J Adv Comput Sci Appl. 2017;8:158–65.
  3. Chaurasia V, Pal S. A novel approach for breast cancer detection using data mining techniques. In: International journal of innovative research in computer and communication engineering (an ISO 3297: 2007 certified organization), vol. 2; 2017.
  4. Shawe-Taylor J, Sun S. A review of optimization methodologies in support vector machines. Neurocomputing. 2011;74(17):3609–18. · DOI
  5. Zheng B, Yoon SW, Lam SS. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst Appl. 2014;41(4):1476–82. · DOI
  6. Ramachandran P, Girija N, Bhubaneswar T. Early detection and prevention of cancer using data mining techniques. Int J Comput Appl. 2014;97(13):48–53.
  7. Lu J, Keech M. Emerging technologies for health data analytics research: a conceptual architecture. In: 2015 26th international workshop on database and expert systems applications (DEXA). IEEE; 2015. p. 225–9. · DOI
  8. Pritom AI, Munshi MAR, Sabab SA, Shihab S. Predicting breast cancer recurrence using effective classification and feature selection technique. In: 2016 19th international conference on computer and information technology (ICCIT). New York: IEEE; 2016. p. 310–4. · DOI
  9. Huang MW, Chen CW, Lin WC, Ke SW, Tsai CF. SVM and SVM ensembles in breast cancer prediction. PLoS ONE. 2017;12(1):e0161501. · DOI
  10. Chaurasia V, Pal S. Data mining techniques: to predict and resolve breast cancer survivability. Int J Comput Sci Mob Comput (IJCSMC). 2014;3(1):10–22.
  11. Kumar UK, Nikhil MS, Sumangali K. Prediction of breast cancer using voting classifier technique. In: 2017 IEEE international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM). New York: IEEE; 2017. p. 108–14. · DOI
  12. Vijaya Lakshmi IV, Krishnaveni G. Performance assessment by using SVM and ANN for breast cancer mammography image classification. Int J Eng Technol Sci Res. 2017;4(9):620–6.
  13. Chaurasia V, Pal S, Tiwari B. Prediction of benign and malignant breast cancer using data mining techniques. J Algorithms Comput Technol. 2018;12(2):119–26. · DOI
  14. Heidari M, Khuzani AZ, Danala G, Mirniaharikandehei S, Qian W, Zheng B. Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk. In: Medical imaging 2018: imaging informatics for healthcare, research, and applications, vol. 10579. International Society for Optics and Photonics; 2018. p. 105790T. · DOI
  15. Kumar V, Mishra BK, Mazzara M, Verma A. Prediction of malignant & benign breast cancer: a data mining approach in healthcare applications. 2019. arXiv preprint arXiv:1902.03825 . · DOI
  16. Dutta S, Ghatak S, Sarkar A, Pal R, Pal R, Roy R. Cancer prediction based on fuzzy inference system. Smart innovations in communication and computational sciences. Springer: Singapore; 2019. p. 127–36. · DOI
  17. Nourelahi M, Zamani A, Talei A, Tahmasebi S. A model to predict breast cancer survivability using logistic regression. Middle East J Cancer. 2019;10(2):132–8.
  18. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61. · DOI
  19. Sun D, Wang M, Li A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinform (TCBB). 2019;16(3):841–50. · DOI · Paper page
  20. Kumar GR, Ramachandra GA, Nagamani K. An efficient prediction of breast cancer data using data mining techniques. Int J Innovat Eng Technol (IJIET). 2013;2(4):139.
  21. Sangaiah I, Kumar AV. Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: application to breast cancer prediction. Clust Comput. 2018. https://doi.org/10.1007/s10586-018-1702-5 . · DOI

Cited by

Sorted by citations

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
Venue
Journal of Big Data
Published
2019-10-16
Volume
6
Issue
1
ISSN
2196-1115 (ISSN-L)
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review