Goh, Choon-Hian and Ferdowsi, Mahbuba and Gan, Ming Hong and Kwan, Ban-Hoe and Lim, Wei Yin and Tee, Yee Kai and Rosli, Roshaslina and Tan, Maw Pin (2024) Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review. MethodsX, 12. p. 102508. ISSN 2215-0161, DOI https://doi.org/10.1016/j.mex.2023.102508.
Full text not available from this repository.Abstract
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. © 2023 The Author(s)
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | Cited by: 0; All Open Access, Green Open Access |
| Uncontrolled Keywords: | adolescent; adult; age; aged; bibliographic database; Calgary score; Calgary Syncope Symptom Score; Canadian Syncope Risk score; child; classification algorithm; comparative effectiveness; data extraction; data integration; decision tree; diagnostic accuracy; diagnostic test accuracy study; disease risk assessment; Evaluation of Guidelines in Syncope Study; faintness; feature selection algorithm; female; gaussian naive bayes; head-up tilt test; hemodynamic parameters; human; intermethod comparison; k nearest neighbor; learning algorithm; logistic regression analysis; machine learning; male; modified Calgary score; multinomial naive bayes; performance indicator; predictive value; publication; quality control; quality of life; random forest; Review; sensitivity and specificity; signal processing; support vector machine; systematic review; Web of Science |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine > Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 15 Nov 2024 07:55 |
| Last Modified: | 15 Nov 2024 07:55 |
| URI: | http://eprints.um.edu.my/id/eprint/44770 |
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