Supervised Learning Algorithm Comparison in Discharge Status Prediction of Trauma Patients: Empirical Evaluation

By analyzing information from trauma centers, hospitals can identify crucial performance indicators that affect budgets and present growth opportunities, potentially leading to lower mortality rates and improved health status indicators.

This study aims to determine the best-supervised algorithm for diagnosing the discharge status of trauma patients.

This retrospective study used the data, collected by the Kashan Trauma Registry from March 2018 to February 2019. Several supervised algorithms, including Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors, have been evaluated for predicting the discharge status of trauma patients. The performance metrics of accuracy, precision, recall, and F-measure were used. The hold-out technique was applied to train the data.

The Random Forest algorithm had the best performance among the other algorithms. The best accuracy, precision, recall, and F-measure for Gini index were 84/2%, 79/7%, 78/3%, and 76.4%, and for information gain were 84.6%, 79.6%, 76.8%, and 76/20%, respectively.

The results of this research showed that the supervised algorithms, with proper parameter settings, can help diagnose the discharge status of trauma patients. In addition, data balancing can help improve the performance of the algorithms. However, this claim cannot be generalized because it depends on the type of algorithm and the values of the parameters.