We have studied the predictability of the complications after bowel surgery with the dataset provided by the MST.We see that the correlated data and rare event outcomes lead to the modest performance of the models employed. We have done an extensive preprocessing where we went from a total of 375 variables to 51 explanatory variables. We applied 3 variable selection methods to predict severe complications and any complications in 3 different scenarios (before surgery, right after surgery, and after primary stay). We will see that interoperative variables add valuable information (comparing Scenario 1 with Scenario 2). However, the interoperative variables become less important for predicting the complications post-primary stay (in Scenario 3). Generally, the most important variables are general health scores (WHO score and ASA class) except when predicting complications right after surgery then interoperative variables, for example blood loss, seem to contribute more to complications than variables indicating preoperative health. We will see that post-operative variables are selected for modelling complications after the primary hospital stay, but that these models do not perform adequately since not many complications are present after primary hospital stay. We compared a literature based selection, one resulting from performing marginal testing and an AIC forward variable selection. We see that in the literature many variables are considered important. Sadly we did not extract information about the timing or severity of the complications from the literature. The AIC forward seems prone to select correlated variables which often led to the resulting model to overfit. The marginal importance was more restrictive than the literature model. We have seen that the (unweighted) logistic regression severely underestimates the outcomes and that the weighted logistic regression will lead to a more informative model but with more misclassifications. We included the Threat score (TS) and the Matthews correlation coefficient (MCC) in our validation and confirmed that these are valid measures for scoring the performace of models with a rare outcome.

In cases that the voltage source is made of multiple cos’s and we can compensate the current, it is always possible with the first Cauer form. This is caused by the shape of the found transfer function Z(s). This transfer function is a fraction where the odd powers of s are in the numerator, while in the denominator are only even powers of s. In this way the conditions 1 and 3 in chapter 4.2 are satisfied. For this special type of Z(s) satisfying condition 2, is having positive coefficients in the First Cauer form, hence the conclusion. In the tables in this thesis, it seems that when the first Cauer form works, the other forms areworking as well. It seems that the method used in this thesis to find a controller only works for small R, L and C. In the example the improved PF is optimal, like one would expect. I conclude that the method used in this thesis is working fine.