| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02054 | |
| Number of page(s) | 10 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802054 | |
| Published online | 17 November 2023 | |
Artificial Intelligence and Machine Learning in Prediction of Total Hip Arthroplasty Outcome: A Bibliographic Review
1 Doctoral Program of Information System, School of Postgraduates Studies, Universitas Diponegoro, Semarang, 50275, Central Java, INDONESIA
2 Department of Informatics, Faculty of Computer Science, University of Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, 60294, East Java, INDONESIA
3 Department of Mechanical Engineering, Faculty of Engineering, Universitas Diponegoro, Semarang, 50275, Central Java, INDONESIA
4 Department of Anatomy, Faculty of Medicine, Universitas Diponegoro, Semarang, 50275, Central Java, INDONESIA
* Corresponding author: intanyuniarpurbasari@students.undip.ac.id
This study investigates the current research trends on the adoption of artificial intelligence and machine learning techniques to predict the outcome of total hip arthroplasty (THA) or total hip replacement (THR) procedure using bibliometric analysis. A total of 102 publications from articles, review, and conference papers were included. The study analysed the network of authors, keywords, citations, and collaboration between authors on the application of artificial intelligence and machine learning to predict the outcome of THA. Regression-based and tree-based machine learning techniques were utilized in the majority of research because they are simpler to comprehend when there are elements involved in the prediction of results. All models had moderate to excellent (AUROC values from 0.71 to 0.97) discrimination ability in making the prediction.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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