Prediction of acute hypotension episodes using Logistic Regression model and Support Vector Machine: A comparative study

Acute hypotension episodes are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prediction of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study new physiological time series are generated based on heart rate, systolic blood pressure, diastolic blood pressure and mean blood pressure time series. Statistical features of these time series are extracted and patients whom are exposed to acute hypotension episodes in future 1 hour time interval and whom are not, are classified based on these features and with the aid of Logistic Regression (LR) model and Support Vector Machine (SVM) classifiers. The best accuracy of classification was 88% with applying SVM classifier and based on selected features which were selected with genetic algorithm.