ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES

Florije ISMAİLİ; Luzana SHABANİ; Bujar RAUFİ; Jaumin AJDARİ; Xhemal ZENUNİ

doi:https://doi.org/10.17261/Pressacademia.2017.605

Article Details

ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES

Authors:

Florije ISMAİLİ | Luzana SHABANİ | Bujar RAUFİ | Jaumin AJDARİ | Xhemal ZENUNİ

Publisher:

PressAcademia

Main Topics:

Social Sciences

Journal:

PressAcademia Procedia 2017 Vol.5 Issue 1, pp.310-316

Document Type:

Article

Publication Date:

6/2017

Document Language:

Turkish

Document ID (Org No.):

2000776656

Frequency:

Tri-annual

eISSN:

2459-0762

DOI:

https://doi.org/10.17261/Pressacademia.2017.605

Abstract:

 


Cancer is one of the crucial causes of death for both men and
women. All over the world, breast cancer is one of the leading cause of cancer
deaths in women. The most effective way to reduce cancer death is to detect it
earlier but the detection of cancer in early stages is not an easy process. As
result, many researches are focused on developing different systems for breast
cancer detection. In this paper we have discussed various data mining
approaches that have been utilized for breast cancer diagnosis and prognosis.
We have proposed a breast cancer prediction framework consisting of four main
modules: Data Collection, Data Preprocessing, Feature Selection, and
Classification. Evaluation results are provided as well. The goal is to find
the best combination for feature extraction algorithm and classification
algorithm, which will improve the accuracy of mammograms classification
process. 





 


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Article Details

ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES

Authors: Florije ISMAİLİ | Luzana SHABANİ | Bujar RAUFİ | Jaumin AJDARİ | Xhemal ZENUNİ

Publisher: PressAcademia

Main Topics: Social Sciences

Journal: PressAcademia Procedia 2017 Vol.5 Issue 1, pp.310-316

Document Type: Article

Publication Date: 6/2017

Document Language: Turkish

Document ID (Org No.): 2000776656

Frequency: Tri-annual

eISSN: 2459-0762

DOI: https://doi.org/10.17261/Pressacademia.2017.605

Issues

Authors:

Florije ISMAİLİ | Luzana SHABANİ | Bujar RAUFİ | Jaumin AJDARİ | Xhemal ZENUNİ

Publisher:

PressAcademia

Main Topics:

Social Sciences

Journal:

PressAcademia Procedia 2017 Vol.5 Issue 1, pp.310-316

Document Type:

Article

Publication Date:

6/2017

Document Language:

Turkish

Document ID (Org No.):

2000776656

Frequency:

Tri-annual

eISSN:

2459-0762

DOI:

https://doi.org/10.17261/Pressacademia.2017.605