Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review

• The first systematic review on syncope classification using machine learning (ML) algorithms includes an evaluation of ML algorithm performance in predicting syncope based on hemodynamic parameters. • The machine learning algorithms have demonstrated their capability to detect syncope with a sensitivity of 88.8%, specificity of 81.5%, and an overall accuracy of 85.8%. • Integration of ML in syncope diagnosis holds the potential to simplify diagnosis with a reduced set of parameters for more efficient assessments and, as a result, reduce unnecessary hospital investigations. 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.