The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Yaghoubzadeh Reyhaneh; Kamel Seyed Reza; BARZEGAR HOSSEIN; Moshajeri Sanati Bahareh

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Multidisciplinary Cancer Investigation Year:2021 | Volume:5 | Issue:1 Page(s): 0-0

Download Full-Text

View:

171

Download:

158

Cites:

Information Journal Paper

Title

The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis

Author(s)

Yaghoubzadeh Reyhaneh | Kamel Seyed Reza | BARZEGAR HOSSEIN | Moshajeri Sanati Bahareh | Issue Writer Certificate

Keywords

Data Mining

Breast Neoplasms

Support Vector Machine

Binary Bat

Algorithms

Abstract

Introduction: The early diagnosis of breast cancer as prevalent cancer among women, is a necessity in the research on cancers since it could simplify the clinical management of other patients. The importance of the classification of breast cancer patients into high-or low-risk groups has led research groups in the biomedical and informatics departments to evaluate and use computer techniques such as Data Mining. To date, various methods have been used for breast cancer diagnosis which has shown unfavorable accuracy due to issues such as computational complexities and prolonged implementation. Methods: The present study aimed to apply the feature selection method based on the Binary Bat algorithm (BBA) to increase the accuracy of the breast cancer diagnosis. Feature selection is carried out to select the most important features from a dataset. We applied the naï ve bayes (NB), Support Vector Machine (SVM), and J48 Algorithms in MATLAB software; based on the dataset obtained from Wisconsin to evaluate the accuracy, sensitivity, and diagnostic criteria of the proposed model. Results: The BBA had 99. 28%, 96. 43%, and 92. 86% accuracy in SVM, NB and J48 Algorithms, respectively. Conclusions: According to the results, the feature selection technique, along with the BBA and SVM, yielded the most accurate results regarding breast cancer detection.

Cites

No record.

References

Cite

APA: Copy

Yaghoubzadeh, Reyhaneh, Kamel, Seyed Reza, BARZEGAR, HOSSEIN, & Moshajeri Sanati, Bahareh. (2021). The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis. MULTIDISCIPLINARY CANCER INVESTIGATION, 5(1), 0-0. SID. https://sid.ir/paper/765266/en

Vancouver: Copy

Yaghoubzadeh Reyhaneh, Kamel Seyed Reza, BARZEGAR HOSSEIN, Moshajeri Sanati Bahareh. The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis. MULTIDISCIPLINARY CANCER INVESTIGATION[Internet]. 2021;5(1):0-0. Available from: https://sid.ir/paper/765266/en

IEEE: Copy

Reyhaneh Yaghoubzadeh, Seyed Reza Kamel, HOSSEIN BARZEGAR, and Bahareh Moshajeri Sanati, “The Use of the Binary Bat Algorithm in Improving the Accuracy of Breast Cancer Diagnosis,” MULTIDISCIPLINARY CANCER INVESTIGATION, vol. 5, no. 1, pp. 0–0, 2021, [Online]. Available: https://sid.ir/paper/765266/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops