ArticleBiology2021
Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test.
Article in Biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- From Mechanism to Management in Vasovagal Syncope: An Integrative Review.Journal of clinical medicine · 2026Review
- Deep Learning-Based Early Prediction of Syncope Onset During Tilt Table Testing via Temporal Convolutional Autoencoder Anomaly Detector.Annals of biomedical engineering · 2026Article
- Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.Frontiers in neuroinformatics · 2026Article
- Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review.MethodsX · 2024Review
- Classification of vasovagal syncope from physiological signals on tilt table testing.Biomedical engineering online · 2024Article
- Can Artificial Intelligence Enhance Syncope Management?: AJACC. Advances · 2023Review
- Quantitative Complexity Theory (QCT) in Integrative Analysis of Cardiovascular Hemodynamic Response to Posture Change.Life (Basel, Switzerland) · 2023Article
- Diversity of Hemodynamic Reactive Profiles across Persons-Psychosocial Implications for Personalized Medicine.Journal of clinical medicine · 2022Article
- Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm.BioMed research international · 2022Article
- Diagnosing Neurally Mediated Syncope Using Classification Techniques.Journal of clinical medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Syncope is the medical condition of loss of consciousness triggered by the momentary cessation of blood flow to the brain. Machine learning techniques have been established to be very effective way to address such problems, where a class label is predicted for given input data. This work presents a Support Vector Machine (SVM) based classification of neuro-mediated syncope evaluated using train-test-split and K-fold cross-validation methods using the patient's physiological data collected through the Head-up Tilt Test in pure clinical settings. The performance of the model has been analyzed over standard statistical performance indices. The experimental results prove the effectiveness of using SVM-based classification for the proactive diagnosis of syncope.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.