Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test

SIMPLE SUMMARY: Syncope is a medical condition triggered by short-lived interruption of the oxygen supply to the brain, which may result in free fall or accidents. The diagnosis of syncope is a challenging task, as various other states of altered consciousness present with the same symptoms as syncope. This work uses historical medical data for the diagnosis of syncope using sophisticated computing solutions. The experimental results prove the effectiveness of the approach, leading to the proactive prediction of syncope. 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.