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

Choon-Hian Goh1,2, Mahbuba Ferdowsi1,2, Ming Hong Gan1

  • 1Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia.

Methodsx
|January 1, 2024
PubMed
Summary

Machine learning (ML) algorithms significantly enhance syncope diagnosis by analyzing hemodynamic data from Head-up Tilt Table Tests (HUTT). These ML approaches offer improved accuracy over traditional scoring systems, aiding in better patient outcomes.

Keywords:
ClassificationMachine learningMethodology for conducting a systematic literature reviewSyncope diagnosisSystematic review

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Biomedical Engineering

Background:

  • Syncope, a sudden, temporary loss of consciousness, presents diagnostic challenges.
  • Existing diagnostic protocols for syncope may be suboptimal.
  • Machine learning (ML) offers potential for improved diagnostic accuracy.

Purpose of the Study:

  • To systematically evaluate machine learning (ML) algorithms for syncope diagnosis.
  • To compare the performance of ML algorithms against traditional point scoring protocols.
  • To assess the utility of ML in analyzing hemodynamic parameters during Head-up Tilt Table Tests (HUTT).

Main Methods:

  • Systematic literature search of IEEE Xplore, Web of Science, and Elsevier (Jan 2011 - Sep 2021).
  • Inclusion of studies using ML for syncope detection with HUTT-monitored hemodynamic parameters in individuals aged five and above.
  • Data extraction included participant demographics, syncope protocols, ML types, hemodynamic parameters, and performance metrics.

Main Results:

  • Ten studies involving 1205 participants (aged 5-82 years) met the inclusion criteria.
  • Overall ML algorithm performance: 88.8% sensitivity, 81.5% specificity, and 85.8% accuracy.
  • ML algorithms require fewer parameters than traditional scoring methods for syncope diagnosis.

Conclusions:

  • Machine learning demonstrates superior performance in syncope diagnosis compared to traditional scoring systems.
  • Integration of ML can reduce unnecessary hospital admissions and improve diagnostic precision.
  • Future research with larger datasets is expected to further enhance ML applications in syncope management.