Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification

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.

Hosseini Alast Zahra Sadat; KEYVANPOUR MOHAMMAD REZA

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

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

Download Full-Text

View:

Download:

Information Seminar Paper

Title

Fuzzy-ID3 Algorithm for Breast Cancer Disease Classification

Author(s)

Hosseini Alast Zahra Sadat | KEYVANPOUR MOHAMMAD REZA

Keywords

Fuzzy Decision tree

ID3

Breast cancer

Classification

Information entropy

Abstract

In terms of death rates, Breast cancer comes in second, among women with cancer. Despite the fact that cancer cells grow in a multistep process involving a number of different types of cells, prevention of Breast cancer stays a challenge inside the modern world. As a method of Breast cancer detection, this paper proposes ENTROPYMOC strategy, a Fuzzy Decision tree with a new formula of Entropy. It aims to improve the Classification accuracy, precision, recall and F1-Measure of the decision tree by overcoming the limitations of the ID3 algorithm, which is not able to classify continuous-valued data. In the field of machine learning, Fuzzy Decision trees are becoming increasingly popular. This algorithm reduces the complexity of the logarithmic entropy formula by simplifying the Shannon entropy principle. WBCD (Original), WDBC (Diagnostic) and Coimbra datasets are used to test the improved algorithm. Based on the experimental results, the improved fuzzy-ID3 algorithm outperforms the other four Classification algorithms (SVM, Naï, ve Bayes, Random forest and FID3) in terms of accuracy. In Coimbra dataset, accuracy increased by 3. 448%.

Cites

No record.

References

No record.

Cite

APA: Copy

Hosseini Alast, Zahra Sadat, & KEYVANPOUR, MOHAMMAD REZA. (). . . SID. https://sid.ir/paper/1047258/en

Vancouver: Copy

Hosseini Alast Zahra Sadat, KEYVANPOUR MOHAMMAD REZA. . . Available from: https://sid.ir/paper/1047258/en

IEEE: Copy

Zahra Sadat Hosseini Alast, and MOHAMMAD REZA KEYVANPOUR, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1047258/en

Related Journal Papers