Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review
Choon-Hian Goh,
Mahbuba Ferdowsi,
Ming Hong Gan,
Ban-Hoe Kwan,
Wei Yin Lim,
Yee Kai Tee,
Roshaslina Rosli,
Maw Pin Tan
Affiliations
Choon-Hian Goh
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, Malaysia; Corresponding author.
Mahbuba Ferdowsi
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, Malaysia
Ming Hong Gan
Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia
Ban-Hoe Kwan
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, Malaysia
Wei Yin Lim
Electrical and Computer Systems Engineering, School of Engineering and Advanced Engineering Platform, Monash University Malaysia, Bandar Sunway 47500, Selangor, Malaysia
Yee Kai Tee
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, Malaysia
Roshaslina Rosli
ACT4Health Services and Consultancy, 47300 Petaling Jaya, Malaysia
Maw Pin Tan
Ageing 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, Malaysia
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.
