Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123005046 |
| _version_ | 1827591158322167808 |
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| author | Choon-Hian Goh Mahbuba Ferdowsi Ming Hong Gan Ban-Hoe Kwan Wei Yin Lim Yee Kai Tee Roshaslina Rosli Maw Pin Tan |
| author_facet | Choon-Hian Goh Mahbuba Ferdowsi Ming Hong Gan Ban-Hoe Kwan Wei Yin Lim Yee Kai Tee Roshaslina Rosli Maw Pin Tan |
| author_sort | Choon-Hian Goh |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T01:27:52Z |
| format | Article |
| id | doaj.art-e1fbad87a03b4ba89ca61353df498518 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T01:27:52Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-e1fbad87a03b4ba89ca61353df4985182023-12-10T06:16:12ZengElsevierMethodsX2215-01612024-06-0112102508Assessing the efficacy of machine learning algorithms for syncope classification: A systematic reviewChoon-Hian Goh0Mahbuba Ferdowsi1Ming Hong Gan2Ban-Hoe Kwan3Wei Yin Lim4Yee Kai Tee5Roshaslina Rosli6Maw Pin Tan7Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia; Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia; Corresponding author.Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia; Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, MalaysiaDepartment of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, MalaysiaDepartment of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia; Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, MalaysiaElectrical and Computer Systems Engineering, School of Engineering and Advanced Engineering Platform, Monash University Malaysia, Bandar Sunway 47500, Selangor, MalaysiaDepartment of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia; Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, MalaysiaACT4Health Services and Consultancy, 47300 Petaling Jaya, MalaysiaAgeing and Age-Associated Disorders Research Group, Department of Medicine, Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Department Medical Sciences, Faculty of Healthcare and Medical Sciences, Sunway University, 47500 Bandar Sunway, MalaysiaSyncope 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.http://www.sciencedirect.com/science/article/pii/S2215016123005046Methodology for conducting a systematic literature review |
| spellingShingle | Choon-Hian Goh Mahbuba Ferdowsi Ming Hong Gan Ban-Hoe Kwan Wei Yin Lim Yee Kai Tee Roshaslina Rosli Maw Pin Tan Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review MethodsX Methodology for conducting a systematic literature review |
| title | Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review |
| title_full | Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review |
| title_fullStr | Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review |
| title_full_unstemmed | Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review |
| title_short | Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review |
| title_sort | assessing the efficacy of machine learning algorithms for syncope classification a systematic review |
| topic | Methodology for conducting a systematic literature review |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123005046 |
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