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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