  Investigation and analysis of patient outcomes, including in-hospital
mortality and length of stay, are crucial for assisting clinicians in
determining a patient's result at the outset of their hospitalization and for
assisting hospitals in allocating their resources. This paper proposes an
approach based on combining the well-known gray wolf algorithm with frequent
items extracted by association rule mining algorithms. First, original features
are combined with the discriminative extracted frequent items. The best subset
of these features is then chosen, and the parameters of the used classification
algorithms are also adjusted, using the gray wolf algorithm. This framework was
evaluated using a real dataset made up of 2816 patients from the Imam Ali
Kermanshah Hospital in Iran. The study's findings indicate that low Ejection
Fraction, old age, high CPK values, and high Creatinine levels are the main
contributors to patients' mortality. Several significant and interesting rules
related to mortality in hospitals and length of stay have also been extracted
and presented. Additionally, the accuracy, sensitivity, specificity, and auroc
of the proposed framework for the diagnosis of mortality in the hospital using
the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively.
According to the framework's findings, adding frequent items as features
considerably improves classification accuracy.
