Top 100 most-cited publications on breast cancer and machine learning research: a bibliometric analysis

Tengku Muhammad Hanis; Md Asiful Islam; Kamarul Imran Musa

doi:10.2174/0929867328666211108110731

Top 100 most-cited publications on breast cancer and machine learning research: a bibliometric analysis

Tengku Muhammad Hanis, Md Asiful Islam^*, Kamarul Imran Musa

^*Corresponding author for this work

Metabolism and Systems Science

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

124 Downloads (Pure)

Abstract

Background: Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer.

Objective: This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies.

Methods: Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications.

Results: The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany, and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning.

Conclusion: Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.

Original language	English
Pages (from-to)	1426-1435
Number of pages	10
Journal	Current medicinal chemistry
Volume	20
Issue number	8
Early online date	17 Jan 2022
DOIs	https://doi.org/10.2174/0929867328666211108110731
Publication status	Published - Mar 2022

Bibliographical note

Funding Information:
The authors would like to acknowledge the Ministry of Higher Education Malaysia for its financial support.

Funding Information:
This study was funded by Fundamental Research Grant Scheme (FRGS), Higher Education, Malaysia (FRGS/1/2019/SKK03/USM/02/1).

Publisher Copyright:
© 2022 Bentham Science Publishers.

Keywords

Bibliometrics
Breast cancer
Machine learning
Research output
Research productivity
Research trend

ASJC Scopus subject areas

Biochemistry
Molecular Medicine
Pharmacology
Drug Discovery
Organic Chemistry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2174/0929867328666211108110731

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Cite this

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abstract = "Background: Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer. Objective: This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies. Methods: Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications. Results: The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany, and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning. Conclusion: Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.",

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Top 100 most-cited publications on breast cancer and machine learning research: a bibliometric analysis

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this