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Investigation and analysis of patient outcomes, including in-hospitalmortality and length of stay, are crucial for assisting clinicians indetermining a patient's result at the outset of their hospitalization and forassisting hospitals in allocating their resources. This paper proposes anapproach based on combining the well-known gray wolf algorithm with frequentitems extracted by association rule mining algorithms. First, original featuresare combined with the discriminative extracted frequent items. The best subsetof these features is then chosen, and the parameters of the used classificationalgorithms are also adjusted, using the gray wolf algorithm. This framework wasevaluated using a real dataset made up of 2816 patients from the Imam AliKermanshah Hospital in Iran. The study's findings indicate that low EjectionFraction, old age, high CPK values, and high Creatinine levels are the maincontributors to patients' mortality. Several significant and interesting rulesrelated to mortality in hospitals and length of stay have also been extractedand presented. Additionally, the accuracy, sensitivity, specificity, and aurocof the proposed framework for the diagnosis of mortality in the hospital usingthe SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively.According to the framework's findings, adding frequent items as featuresconsiderably improves classification accuracy.

GWO-FI: A novel machine learning framework by combining Gray Wolf  Optimizer and Frequent Itemsets to diagnose and investigate effective factors  on In-Hospital Mortality and Length of Stay among Kermanshahian  Cardiovascular Disease patients