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

TL;DR

This paper introduces GWO-FI, a machine learning framework combining the Gray Wolf Optimizer and frequent itemset mining to improve diagnosis and understanding of factors affecting in-hospital mortality and length of stay among cardiovascular patients.

Contribution

It presents a novel hybrid approach that integrates feature selection and parameter tuning using Gray Wolf Optimizer with frequent itemset features for better predictive accuracy.

Findings

High classification accuracy (0.9961) for mortality prediction.

Identified key factors like low Ejection Fraction and high Creatinine levels.

Frequent item features significantly enhance model performance.

Abstract

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.