Prediction of Syncope based on Physiological Data Analysis using Decision Tree Algorithm

摘要

In this paper, we propose a method to predict the chance of Syncope or falling due to temporary loss of consciousness, based on an analysis of physiological data. We consider an authentic data set containing the measures of electroencephalogram, blood pressure, heart rate and blood circulation during different human activities, including fall. We experiment with this data set by employing supervised learning techniques, in order to predict Syncope efficiently. The proposed model gives an accuracy of 82.514% in Syncope prediction.