Title: Bibliometric analysis of renal cancer disease and cancer stage prediction in machine learning algorithms

Authors: Neha Chankhore; Kavita Singh

Addresses: School of CSIT, Symbiosis Skill and Professional University, Pune, India ' Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India

Abstract: Renal cancer is one of the deadliest diseases according to the WHO ranking. Renal cancer spreads in the kidney and nearby areas. Machine learning algorithms are used to identify the association between patient personal details and computed tomography CT) images to recognise the presence of cancer. The paper studies the Scopus database for bibliometric analysis of kidney cancer prediction by analysing research in field of authors, keywords, institutions, countries, and funding agency. The 368 research documents were from the Scopus database between year 2018 to December 30, 2022. Bibliometric analysis is analysis of the research papers published statistically in the form of articles, conference papers, journals, and reviews, which enhance the impact of publications in research fields around the world. The visual analysis is performed using the open-source tools GPS Visualiser, Gephi, VOS viewer, Sciences Scape, and word cloud, which helps better understanding of the perspectives discussed in research papers.

Keywords: machine learning; computer tomography; artificial intelligence; prediction; bibliometric analysis; visual analysis.

DOI: 10.1504/IJAISC.2024.146627

International Journal of Artificial Intelligence and Soft Computing, 2024 Vol.8 No.4, pp.305 - 320

Received: 02 Jun 2023
Accepted: 11 Aug 2024
Published online: 09 Jun 2025 *

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