Academic Journal
Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review
| Title: | Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review |
|---|---|
| Authors: | Goh, CH, Ferdowsi, M, Gan, MH, Kwan, BH, Lim, WY, Tee, YK, Rosli, R, Tan, MP |
| Source: | urn:ISSN:2215-0161 ; Methodsx, 12, 102508 |
| Publisher Information: | Elsevier |
| Publication Year: | 2024 |
| Collection: | UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: | 46 Information and Computing Sciences, 32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Bioengineering, Classification, Machine learning, Methodology for conducting a systematic literature review, Syncope diagnosis, Systematic review, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 32 Biomedical and Clinical Sciences, anzsrc-for: 3202 Clinical Sciences, anzsrc-for: 0912 Materials Engineering |
| Description: | Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4–96.1%), specificity of 81.5% (95% CI: 69.8–92.8%) and accuracy of 85.8% (95% CI: 78.6–92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients. |
| Document Type: | article in journal/newspaper |
| Language: | unknown |
| Relation: | https://hdl.handle.net/1959.4/108826; https://doi.org/10.1016/j.mex.2023.102508 |
| DOI: | 10.1016/j.mex.2023.102508 |
| Availability: | https://hdl.handle.net/1959.4/108826 https://doi.org/10.1016/j.mex.2023.102508 |
| Rights: | open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: | edsbas.92C1FE43 |
| Database: | BASE |
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