What is it about?

The incidence of cancer is rising dramatically on a global scale. Many people receive a late-stage diagnosis even though cancer is preventable and well-treated in its early stages. Furthermore, cancer frequently returns following protracted treatment. Therefore, it is essential to anticipate cancer recurrence to proactively pursue specific treatments. The study reviews literature done on machine learning algorithms for cancer prediction using bibliometric review and Vosviewer. The studies that were taken into consideration for this research were obtained from the Web of Science database. A range of search terms, such as pertinent keywords (Machine learning, cancer detection, cancer prediction), were utilized to examine the titles, keywords, and abstracts of articles inside the database covering the years 2014 through 2023. 1914 documents were used for the study. Articles published in IEEE Access have a greater impact, but Scientific Reports received more citations than articles in other journals—roughly 1018 citations for the chosen documents come from articles published in this journal. Research on the International evaluation of an AI system for screening breast cancer had the highest citations 1059. India has the highest documents of 430 representing 22.419%. For citations, the USA has the highest of 122,072. Egyptian Knowledge Bank EKB has the highest documents of 61 representing 3.180%. The National Natural Science Foundation of China (NSFC) has the highest documents of 131 representing 6.830%. Machine learning must be incorporated into clinical practice and trials due to the changing nature of cancer and how it is treated over time.

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Why is it important?

The incidence of cancer is rising dramatically on a global scale. Many people receive a late-stage diagnosis despite though cancer is preventable and well-treated in its early stages. Furthermore, cancer frequently returns following protracted treatment. Therefore, it is essential to anticipate cancer recurrence to proactively pursue specific treatments.

Perspectives

The study reviews the use of machine learning algorithms for cancer prediction through a bibliometric analysis using data from the Web of Science database. Covering research from 2014 to 2023, it examines 1,914 documents, identifying key trends, influential publications, and global contributions. The study highlights the rising importance of machine learning in early cancer detection and prediction, emphasizing its potential to improve outcomes by anticipating cancer recurrence and personalizing treatments. This research is crucial as it underscores the need to integrate AI into clinical practice, which could revolutionize cancer care and significantly reduce mortality rates.

Mr. Isaac Atta Senior Ampofo
University of Liverpool

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This page is a summary of: Machine Learning Algorithm for Cancer Prediction: A Bibliometric Review, January 2024, Springer Science + Business Media,
DOI: 10.1007/978-3-031-66428-1_43.
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