Key result
Logistic regression outperforms other machine learning models for classifying neurally mediated syncope on tilt testing.
Why the study?
Diagnosing syncope is challenging because similar symptoms are seen in seizures, vertigo, stroke, and coma.
Does a logistic regression machine learning model accurately diagnose neurally mediated syncope using physiological data from head-up tilt testing?
Observational (n=687)
No
Does a logistic regression machine learning model accurately diagnose neurally mediated syncope using physiological data from head-up tilt testing?
Machine learning algorithms, particularly logistic regression, demonstrate promising capability in diagnosing neurally mediated syncope from head-up tilt test physiological data.
Logistic regression may aid tilt-test syncope classification; extends ML applications but remains hypothesis-generating and should not yet change practice.
Syncope is a medical condition resulting in the spontaneous transient loss of consciousness and postural tone with spontaneous recovery. The diagnosis of syncope is a challenging task, as similar types of symptoms are observed in seizures, vertigo, stroke, coma, etc. The advent of Healthcare 4.0, which facilitates the usage of artificial intelligence and big data, has been widely used for diagnosing various diseases based on past historical data. In this paper, classification-based machine learning is used to diagnose syncope based on data collected through a head-up tilt test carried out in a purely clinical setting. This work is concerned with the use of classification techniques for diagnosing neurally mediated syncope triggered by a number of neurocardiogenic or cardiac-related factors. Experimental results show the effectiveness of using classification-based machine learning techniques for an early diagnosis and proactive treatment of neurally mediated syncope.
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Hussain et al. (2021) conducted an observational in Syncope or dizziness upon standing up (n=687). Classification-based machine learning (Logistic Regression) vs. Other machine learning algorithms (Support Vector Machine, Decision Tree, Gaussian Naïve Bayes, Multinomial Naïve Bayes, k-Nearest Neighbor) was evaluated on Classification performance (accuracy, precision, recall, F1-score, AUC-ROC) for diagnosing neurally mediated syncope. Logistic regression outperformed other machine learning models in overall accuracy, recall, F1-score, and AUC-ROC for classifying neurally mediated syncope using physiological data from head-up tilt testing.
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