## 5 Discussion  
Many studies are using the area under receiver operating characteristics curve (AUC) as an evaluation metric. In this study, we prefer to use the confusion matrix for evaluation and direct quantification of error metrics of concern (e.g., sensitivity, precision). However, the calculated AUC for our optimised classifier is 0.91 for comparison purposes. This result, when compared to several recent studies is satisfactory. For instance, a recent study focusing on a special profile of ICU patients reported an AUC of 0.70 using a developed novel mortality prediction SOFA-RV [35]. Another study [12] that evaluates the Super ICU Learner Algorithm (SICULA) and its predictive power applied to MIMIC II database reported an AUC of 0.88 on average under specific conditions and 0.94 on average when applied to an external validation set with calibration. The study of [PERSON] et al. [11] reported an AUC of 0.848. [PERSON] et al. proposed an unsupervised feature learning algorithm that extracts features automatically from the clinical multivariate time-series. [PERSON] et al. applied their algorithm to the MIMIC-II [17] dataset with a prediction horizon extending to 30 days. The study in Reference [8] that developed a convolutional neural network (CNN) as a deep learning approach to predict mortality risk at ICU reported, as the highest performance, an AUC of 0.87, a precision of 0.7443 and a recall of 0.8188. The developed model used the variables of heart and respiration rate, systolic and diastolic blood pressure obtained from the MIMIC-III dataset [18]. [PERSON] et al. [8] referred to the difficulties and limitations of using electronic medical report (EMR) data, similar to our dataset, for the purpose of mortality prediction at ICU. [PERSON] et al. in their study [36] of sepsis early prediction, which is a lead cause of morbidity and mortality of ICU patients, developed a machine learning model that reported an AUC of 0.83-0.85 for a prediction horizon of 12 down to 4 h prior to clinical recognition. [PERSON] et al. used an EMR data with high-resolution vital signs time-series obtained from the MIMIC-III dataset [18]. Two medical studies [32; 33] reported an observed relevance between the low pulse pressure and mortality risk. Which is consistent with our finding of considering the pulse pressure as an independent variable from which both statistical and dynamic features can be extracted to inform mortality prediction. Moreover, the medical study in Reference [37] concludes the relevance between the widened (high) pulse pressure and the mortality risk for a special profile of critically ill patients. This conclusion as well is consistent with our finding, as we referred to the statistical and dynamic features of the pulse pressure which will indicate either abnormally high or low levels of pulse pressure. It is important to note that each study has different conditions, different objectives, different datasets, parameters and variables and predictive models. At the feature extraction phase, the variation of results with different categories shows that a set of features can be efficient with a group of patients (i.e., correctly classified) but the same set of features can be inefficient or confusing to another group of patients (i.e., misclassified). For instance, statistical features classified correctly 83 TP's and 148 TN's, on the other hand, dynamic features classified correctly 32 TP's and 247 TN's. Considering the patient identity, it is found that dynamic features correctly classified 18 TP's and 116 TN's that the statistical features misclassified. The same observation is noticed with \(PP\) extracted features (45 TP's and 222 TN's) and those features extracted from both \(SBP\) and \(DBP\) together (72 TP's and 199 TN's). The difference in this situation is that \(PP\) is a result of a linear combination between \(SBP\) and \(DBP\), however, \(PP\) extracted features correctly classified 14 TP's and 58 TN's that are misclassified by \(SBP/DBP\) extracted features.