## 5 Discussion
Hence, the influence of features should be evaluated on a subject-basis in addition to error metrics. Another observation is that the physiological features of _low-RR_ and _low-SPO2 count_ do not correctly classify any true positive patient despite their physiological basis when presented as the only input features. However, their contribution is significant when combined with the consistent set of features as shown at the feature fine-tuning phase. Therefore, excluding a feature has to be done after that it has been tested in combination with different groups of features especially if the extracted feature has a physiological basis. At the fine-tuning phase, we have to note that this process is based on feature-vector-level not dimension-level as a single extracted feature may include multiple dimensions (e.g., the mean within each window for a specific vital sign). Which is in contrast with conventional feature selectiontechniques that rely on selecting the most relevant dimensions regardless of the interpretation of the selected dimensions. The initial modification is excluding both statistical and dynamic features extracted from \(HR\) in order to enhance the performance. This modification is required as many of the cardiovascular patients in this study have common cardiac diseased behaviour, which confuses the classifier. Moreover, the heart acts as one of the main actuators in the human control system responding to different types of excitations. Hence, \(HR\) disturbances might not be sufficient to predict mortality, leading to a high false alarm rate. Ultimately, considering the cardiovascular patients specifically, HR statistical characteristics, as well as their HR dynamic features are both technically confusing to mortality prediction. Moreover, the enhancement of detecting more TP's by presenting some dynamic features in an aggregate form can be interpreted by the fact that the concurrence of vital signs deterioration is partially a sufficient mortality indicator but not a necessary one. In other words, total deterioration implies mortality but not vice versa. Introducing the _correlation coefficients_ feature with absolute values in addition to real values provides an improvement. Both absolute and real values help the linear classifier to distinguish between the instances based on the correlation strength and correlation sign respectively. Restricting the _crossing-the-mean count_ to \(SBP\) and \(RR\) caused an improvement. Thus, observation-to-observation variability of both vital signs even for a relatively low sampling rate (i.e., 0.5-1 sample/hour) is more informative than the other vital signs for resting patients such as ICU patients. As the main objective of this study is to engineer feature that can provide clinical insight about mortality prediction, it is important to refer to the decision tree classifiers. As one of the decision trees advantages is model interpretability in terms of the input attributes. However, some shortages are present in decision trees in contrast with SVM's that supported the choice of the latter. These shortages are mainly the greedy nature of the algorithm, local optimisation, prone to overfitting and expensive computational cost compared to linear hard margin SVM in which there are no hyperparameters to optimise. Moreover, we based our study on the optimal margin hypothesis which is not provided by decision trees in contrast with SVM's. For comparison reasons, a decision tree analysis is applied to the final set of features. A CART algorithm decision tree (MATLAB 2017) is used with the following settings: the splitting criterion of _gdi_, minimum parent size of 368, minimum leaf size of 184, maximum splits of 450 and pruning based on classification error criterion. The classification output of the optimised decision tree is as follows: sensitivity of 41.2% precision of 42.42%, F1-score 41.80% and accuracy of 52.22%. It is obvious that the results are poor compared to the results of linear hard margin SVM.