Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990-2022).

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.

Related collections

Author and article information

Journal

Journal ID (iso-abbrev): Artif Intell Med

Title: Artificial intelligence in medicine

Publisher: Elsevier BV

ISSN (Electronic): 1873-2860

ISSN (Print): 0933-3657

Publication date (Electronic): May 2023

Volume: 139

Affiliations

[1 ] The University of Queensland Rural Clinical School, Locked Bag 9009, Toowoomba DC 4350, Queensland, Australia. Electronic address: joshua.sheehy@uq.net.au.

[2 ] The University of Queensland Rural Clinical School, Locked Bag 9009, Toowoomba DC 4350, Queensland, Australia. Electronic address: hamish.rutledge@uq.net.au.

[3 ] School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore. Electronic address: rajendra_udyavara_acharya@np.edu.sg.

[4 ] School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore. Electronic address: hwloh002@suss.edu.sg.

[5 ] University of Southern Queensland, 37 Sinnathamby Blvd, Springfield Central, Qld 4300, Australia. Electronic address: Raj.Gururajan@usq.edu.au.

[6 ] University of Southern Queensland, 487 - 535 West Street, Toowoomba, Qld 4350, Australia. Electronic address: Xiaohui.Tao@usq.edu.au.

[7 ] University of Southern Queensland, 487 - 535 West Street, Toowoomba, Qld 4350, Australia. Electronic address: xujuan.zhou@usq.edu.au.

[8 ] School of Computer Science, Queensland University of Technology, Brisbane, Australia. Electronic address: y2.li@qut.edu.au.

[9 ] The University of Queensland Rural Clinical School, Locked Bag 9009, Toowoomba DC 4350, Queensland, Australia. Electronic address: t.gurney@uq.edu.au.

[10 ] The University of Queensland Rural Clinical School, Locked Bag 9009, Toowoomba DC 4350, Queensland, Australia. Electronic address: uqskonda@uq.edu.au.

Article

Publisher Item ID: S0933-3657(23)00050-7

DOI: 10.1016/j.artmed.2023.102536

PubMed ID: 37100507

SO-VID: 151d490b-61d9-4905-9b4e-ca33f7d8bb1b

History

Keywords: Prognosis,Prediction,Gynecological oncology,Artificial intelligence,Machine learning

Data availability:

Keywords: Prognosis, Prediction, Gynecological oncology, Artificial intelligence, Machine learning

Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990-2022).

Read this article at

Abstract

Related collections

Artificial Intelligence in Medicine

Author and article information

Journal

Affiliations

Article

History

Comments

Comment on this article

Similar content 213

Cited by 9