  Surgical risk increases significantly when patients present with comorbid
conditions. This has resulted in the creation of numerous risk stratification
tools with the objective of formulating associated surgical risk to assist both
surgeons and patients in decision-making. The Surgical Outcome Risk Tool (SORT)
is one of the tools developed to predict mortality risk throughout the entire
perioperative period for major elective in-patient surgeries in the UK. In this
study, we enhance the original SORT prediction model (UK SORT) by addressing
the class imbalance within the dataset. Our proposed method investigates the
application of diversity-based selection on top of common re-sampling
techniques to enhance the classifier's capability in detecting minority
(mortality) events. Diversity amongst training datasets is an essential factor
in ensuring re-sampled data keeps an accurate depiction of the
minority/majority class region, thereby solving the generalization problem of
mainstream sampling approaches. We incorporate the use of the Solow-Polasky
measure as a drop-in functionality to evaluate diversity, with the addition of
greedy algorithms to identify and discard subsets that share the most
similarity. Additionally, through empirical experiments, we prove that the
performance of the classifier trained over diversity-based dataset outperforms
the original classifier over ten external datasets. Our diversity-based
re-sampling method elevates the performance of the UK SORT algorithm by 1.4$.
