The use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles
Iffah Syafiqah Meor Badi’auzzaman1, Moey Soo Foon2, Mohd. Zulfaezal Che Azemin3, Mohd. Izzuddin Mohd. Tamrin4
1Iffah Syafiqah Meor Badi’auzzaman, Kulliyyah of Allied Health Sciences, International Islamic University, Kuantan, Malaysia.
2Moey Soo Foon, Kulliyyah of Allied Health Sciences, International Islamic University, Kuantan, Malaysia.
3Mohd. Zulfaezal Bin Che Azemin, Kulliyyah of Allied Health Sciences, International Islamic University Kuantan, Malaysia.
4Mohd. Izzuddin Bin Mohd. Tamrin, Kulliyyah of Information and Communication Technology, International Islamic University, Gombak, Malaysia.
Manuscript received on 09 July 2019 | Revised Manuscript received on 21 July 2019 | Manuscript Published on 23 August 2019 | PP: 1344-1355 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I32900789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3290.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is the leading cancer that occurs in women globally. The use of machine learning has been introduced to supplement the work in breast cancer studies. There are undisputed pieces of evidence of the existence of publications pertaining to the use of decision tree in breast cancer-related research. However, little is known regarding the types and frequencies of the searched articles. The main objective of this paper is to unearth the broad variety of articles related to breast cancer research that utilized decision trees. The Scopus database was chosen to examine the trend, frequencies and themes of the related publications from the year 2013 until 2018. The study was also intended to disclose the categories of articles based on the areas of breast cancer that have employed the decision trees method. A total of 259 articles from Scopus database were found to meet the inclusion criteria. The analysis of the frequency of published articles generally shows an upward trend. The majority of articles targeted diagnosis of breast cancer (37.8%) in comparisons with other categories. Even though the number of articles found is adequate, several categories of breast cancer are lacking in publications specifically the survivability, incidence, and recurrence of breast cancer among patients. There is a need to redirect the focus of breast cancer research on these categories for future efforts.
Keywords: Breast Cancer, Mammography, Machine Learning, Decision Tree
Scope of the Article: Machine Learning