A Comparison of Supervised Learning Techniques for Predicting the Mortality of Patients with Altered State of Consciousness

Abstract

The study attempts to identify a potentially reliable supervised learning technique for predicting the outcomes of mortality in an altered state of consciousness (ASC) patients. ASC is a state distinguished from ordinary waking consciousness, which is a common phenomenon in the Emergency Department (ED). Thirty (30) distinctive attributes or features are commonly used to recognize ASC. The study accordingly applied these features to model the prediction of mortality in ASC patients. Supervised learning techniques are found to be suitable for such classification problems. Consequently, the study compared five supervised learning techniques that are commonly applied to evaluate the risk of mortality using health-related datasets, namely Decision Tree, Neural Network, Random Forest, Naí¯ve Bayes, and Logistic Regression. The labeled dataset comprised patient records captured by the Universiti Sains Malaysia hospital’s Emergency Medicine department from June to November 2008. The cleaned dataset was divided into two parts. The larger part was used for training and the smaller part, for evaluation. Since the ratio between training and testing samples varies between individual supervised learning techniques, we studied the performance of the modeled techniques by also varying the proportion of the training data to the dataset. We applied four percentage splits; 66%, 75%, 80%, and 90% to allow for 3-, 4-, 5- and 10-fold cross-validation experiments to evaluate the accuracy of the analyzed techniques. The variation helped to lessen the chance of over fitting, and averaged the effects of various conditions on accuracy. The experiments were conducted in the WEKA environment. The results indicated that Random Forest is the most reliable technique to model for predicting the mortality in ASC patients with acceptable accuracy, sensitivity, and specificity of 70.9%, 76.3%, and 65.5%, respectively. The results are further confirmed by SROC analysis. The findings of the study serve as a fundamental step towards a comprehensive study in the future.